miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma

Accepted Manuscript miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma

Olfat Shaker, Maha Alhelf, George Morcos, Aisha Elsharkawy PII: DOI: Reference:

S1567-1348(17)30113-2 doi: 10.1016/j.meegid.2017.03.030 MEEGID 3110

To appear in:

Infection, Genetics and Evolution

Received date: Revised date: Accepted date:

1 February 2017 27 March 2017 28 March 2017

Please cite this article as: Olfat Shaker, Maha Alhelf, George Morcos, Aisha Elsharkawy , miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Meegid(2017), doi: 10.1016/j.meegid.2017.03.030

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ACCEPTED MANUSCRIPT

Title: miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early

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diagnosis of hepatocellular carcinoma

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Authors : Olfat Shaker*, Maha Alhelf*, George Morcos*, Aisha

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Elsharkawy** Affiliations:

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*Medical Biochemistry and Molecular Biology Department,

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Faculty of Medicine, Cairo University

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** Endemic Medicine and Hepatogastroenterology Department, Faculty of Medicine, Cairo University

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Corresponding author: Olfat Shaker, Professor of Medical

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Biochemistry and Molecular Biology Department, Faculty of Medicine, Cairo University

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E-mail: [email protected] Tel: 01227449192 All authors accepting publication of this manuscript in this journal. This manuscript is not under publication in any other journals.

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ACCEPTED MANUSCRIPT Abstract Background: Hepatocellular carcinoma (HCC) is the fifth most common malignant tumor with an increasing incidence. Hepatitis C virus (HCV) is one of the major risk factors that lead to HCC development. MicroRNAs are

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conserved non-coding RNAs which regulate gene expression at the

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posttranscriptional level. They have been recently identified as important

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regulators that affect carcinogenesis. Of these miRNAs, are miR-221 and miR-101-1, which their aberrant expressions have been reported to play an

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important role in HCC.

Patients and Methods: In this study, we investigated the association between

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miR-221 and miR-101-1 polymorphisms and their expressions and the early prediction of HCC in HCV infected patients. Quantitative real-time PCR

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(qPCR) was done to estimate the expression levels of miRNA-221 and

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miRNA-101-1 in serum. To detect the genotyping of miR-221 and miR-101-

real-time PCR.

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1 related SNPs, DNA was extracted. Then, genotyping was performed using

Results: We found that rs7536540 polymorphism in miR-101-1 is

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significantly associated with development of HCC. In addition, our results

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showed no significant association between rs17084733 polymorphism in miR-221 and HCC occurrence. We confirmed the upregulation of miR-221 and the downregulation of miR-101-1 in HCC. As regards HCV patients, miR-221and miR-101-1 were found to be upregulated. Conclusion: Both miR-221 and miR-101 -1 expression levels may be useful as non invasive diagnostic biomarkers for early prediction of HCC among HCV patients. Keywords: HCC; HCV; miR-101-1; miR-221; SNPs 2

ACCEPTED MANUSCRIPT 1. Introduction Being one of the most frequently diagnosed cancers worldwide, liver cancer is the second leading cause of cancer death in men and the sixth leading cause of cancer-related death in women (Jemal et al., 2011). About

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90% of Hepatocellular carcinoma (HCC) cases arise from cirrhosis, which

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can be attributed to a wide range of factors including chronic viral hepatitis

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B or C (HBV or HCV) infections, alcohol abuse, nonalcoholic steatohepatitis (NASH), autoimmune hepatitis, primary biliary cirrhosis

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(PBC) and carcinogens exposure (Sanyal et al., 2010).

The prevalence of HCC is increasing in the last years. This rising

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incidence may be due to high prevalence of hepatitis C virus (HCV) and its complications (Shaker et al., 2013) and the fact that people born 20 years

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(HBV) (Di Bisceglie, 2009).

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ago or earlier in Egypt have not been vaccinated against hepatitis B virus

microRNAs are small, ~22 nucleotide non-coding RNAs that induce

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translational inhibition of target genes by partial base complementarity to sequences within the target mRNA-3`-UTR. MicroRNAs originate from larger

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RNA Pol II transcripts in the nucleus and are post-transcriptionally processed

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into mature miRNAs with RNase III like enzymes, Drosha in the nucleus and Dicer in the cytoplasm. MicroRNAs recognize target mRNA sequences in the context of an effector RNA–protein complex (RISC, RNA induced silencing complex) in the cytoplasm to inhibit translation of the target mRNA, or initiate its degradation (Bartel, 2004). Like other cancers, the development of HCC is a multistep process with accumulation of genetic and epigenetic changes (Su et al., 2008). Aberrant expression of miRNAs has been observed in various types of 3

ACCEPTED MANUSCRIPT cancers (Lu et al., 2005) and is also associated with the clinical outcome of cancer patients (Jiang et al., 2008). Several miRNAs, deregulated in HCC, such as miR-101-1 and miR-221 have been identified as modulators of cell growth, apoptosis, migration, or invasion (Fornari et al., 2008 and Hang et al., 2009).

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MiR-221 has been reported to be overexpressed in human HCC

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tissues, compared with normal liver tissues or adjacent benign liver tissues

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(Gramantieri et al., 2008). Regarding the down-regulated miR-101-1, it was found that it could sensitize tumor cells to apoptosis and impair the ability of

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cancer cells to form colony in vitro and to develop tumor in vivo(Hang et

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al., 2009).

MiRNA related single nucleotide polymorphisms (miR-SNPs),

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defined as single nucleotide polymorphisms (SNPs) in miRNA genes, miRNA binding site and miRNA processing machinery, can modulate

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miRNA and targeted genes expression so as to affect cancer development, therapeutic efficacy and patient’s prognosis (Ryan et al., 2010).

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Previous studies reported that miR-101-1 G>C polymorphism is

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associated with increased HCC risk (Bae et al., 2012). miR-221 G>A polymorphism is associated with increased risk of papillary thyroid

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carcinoma (He et al., 2005) and acral melanoma (Godshalk et al., 2011) but to the best of our knowledge, no study had declared its association with HCC development.

2. Subjects and methods The present study included 115 Egyptian subjects. They were divided into three groups: Group I: 37 HCC patients with no previous treatment for HCC, 4

ACCEPTED MANUSCRIPT they were 78.4% males with age ranged from (45-75 years). Group II: 46 HCV patients with no previous treatment for HCV infection, 69.6% were males and their age ranged from (38-67). Group III: 32 healthy control subjects with no history of other cancers, 65.6 % were males and their age ranged from (33-65).

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Informed written consent was obtained from the participants in this

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study after ethical committee approval from Medical Biochemistry

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Department, Faculty of Medicine, Cairo University. Patients’ confidentiality was respected as patients were represented in the study by code numbers and

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not by their names with all personal data concealed. The study protocol

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conformed to the ethical guidelines of the 1975 declaration of Helsinki.

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2.1. Blood sample collection and storage: Three mL peripheral blood sample was withdrawn from each subject and

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taken in plain tube for serum separation that was used in detecting all serological markers for HCV, HBV as well as HCV Viral RNA quantitation

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by real time PCR. Serum was stored at –80°C for micro-RNA extraction and

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detection of fold change of the 2 genes (microRNAs 101-1 and 221) using real time PCR

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Two mL whole blood was taken on EDTA for DNA extraction then genotyping of the studied SNPs (rs 7536540 and rs 17084733) using real time PCR. 2.2.

RNA extraction

Total RNA with preserved micro-RNAs was extracted from 200 μL serum by miRNeasy extraction kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. RNA samples were subjected to RNA quantitation 5

ACCEPTED MANUSCRIPT and purity assessment using the NanoDrop® (ND)-1000 spectrophotometer (NanoDrop Technologies, Inc. Wilmington, USA). The extracted micro-RNA then stored at –80°C until use.

2.3.

Reverse transcription (RT) and real-time quantitative PCR

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(qPCR)

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Reverse transcription was carried out on total RNA in a final volume of 20 uL RT reactions (incubated for 60 min at 37°C, followed by 5min at

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95°C) using the miScript II RT kit (Qiagen, Valencia,CA, USA) according to the manufacturer's instructions. Real-time qPCR was performed using a

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MiScript SYBR Green PCR kit (Qiagen, Valencia,CA, USA) and miScript primer assay miR-101-1and miR-221 (Qiagen, Valencia, CA, USA). 20 ng

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of cDNA were used as a template in a total volume of 20 μL reaction with

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the following conditions: denaturation at 95°C for 15 min followed by 40 cycles of 94°C for 15 s, 55°C for 30 s, and 70°C for 34 s, in which

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fluorescence was acquired and detected by Rotor-gene Q Real-time PCR system (Qiagen, USA). After the PCR cycles, melting curve analyses were

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performed to validate the specific generation of the expected PCR product. SNORD 68 was used as an endogenous control. The expression level of

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miR-101-1and miR-221 was evaluated using the ΔCt method. The cycle threshold (Ct) value is the number of qPCR cycles required for the fluorescent signal to cross a specified threshold. ΔCt was calculated by subtracting the Ct values of SNORD from those of target micro-RNAs. ΔΔCt was calculated by subtracting the ΔCt of the control samples from the ΔCt of the cancer samples. The fold change in miR-101-1and miR-221 expression was calculated by the equation 2–ΔΔCt. 6

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2.4.

Detection of genotyping (rs 7536540 & rs 17084733):

2.4.1. DNA extraction: DNA was extracted from whole blood using Qia-amplification DNA

2.4.2. Quantitation and assessment of DNA purity:

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extraction kit (Qiagen, USA) according to the manufacturer's instructions.

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DNA samples were subjected to DNA quantitation and purity assessment

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using the NanoDrop® (ND)-1000 spectrophotometer (NanoDrop Technologies, Inc. Wilmington, USA)

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2.4.3. Genotyping of miR-101-1 and miR-221 related SNPs (rs 7536540

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& rs 17084733):

Genotyping was performed using real-time polymerase chain reaction with

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TaqMan allelic discrimination assay (Applied Biosystems, USA). A predesigned primer/probe sets for the 3 genotypes were used (Applied

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Biosystems, USA). Probes were synthesized with reporter dye FAM or VIC covalently linked at the 5`end and a quencher dye MGB linked to the 3 ` end

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of the probe (Applied Biosystems, USA). DNA amplification was carried out

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in a 25 μl total volume. Real-time PCR was performed using a Rotor gene Q Real Time PCR System (Qiagen, Valencia, CA, USA) with the following

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conditions: after a denaturation time of 10 min at 95oC, 45 cycles at 92oC for 15s then 60oC for 90s for annealing and extension were carried out and fluorescence was measured at the end of every cycle and at the endpoint. 2.5.

Statistical Analysis: Data were coded and entered using the statistical package SPSS

(Statistical Package for the Social Sciences) version 23. Data was summarized using median, minimum and maximum in quantitative data and using frequency (count) and relative frequency (percentage) for categorical data. Comparisons 7

ACCEPTED MANUSCRIPT between quantitative variables were done using the non-parametric KruskalWallis and Mann-Whitney tests (Chan, 2003a). For comparing categorical data, Chi square (2) test was performed. Exact test was used instead when the expected frequency is less than 5 (Chan, 2003b). Correlations between quantitative variables were done using Spearman correlation coefficient (Chan,

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2003c). Genotype and allele frequencies were compared between every 2 groups using chi-square tests. Odds ratio (OR) with 95% confidence intervals was

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calculated. ROC curve was constructed with area under curve analysis performed to detect best cutoff value of miRNA for detection of HCC.

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Multivariate logistic regression analysis was done to detect the independent

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predictors of HCC. P-values less than 0.05 were considered as statistically significant

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3. Results

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Table (1a and 1b) demonstrate the clinicopathological parameters of HCC and HCV patients respectively. Comparing the laboratory data between

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HCC and HCV groups, it was found that there is statistical significance difference in Hb level (P < 0.001), white blood cells count (WBCs) (P= 0.002), platelets

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count (P<0.001), AST level (P <0.001), ALP level (P<0.001), total bilirubin

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level (P<0.001), albumin level (P<0.001) and AFP level (P<0.001). Meanwhile, no statistical significance difference was detected regarding ALT (P=0.214) (table 2). In the present study, miR-221 genotype GA and GG and their alleles (allele A and allele G) show no statistical difference between HCC patients and control group. Meanwhile, there are statistically significant differences in miR-101-1 genotypes CC and GC when comparing HCC to control group, However GG genotype shows no statistical significant differences between 8

ACCEPTED MANUSCRIPT them. At the same time, no statistical significance difference was found by comparing the distribution of miR-101-1 alleles (C and G) in the two groups (Table 3). Our study shows no statistical significance between miR 221genotypes and the age (P=0.1), sex (P=0.332), tumor size (P=0.447),

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portal vein patency (P=0.649), metastasis (P=1) and ascites (P=0.413)

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among HCC patients (Results not shown).

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When studying association between miR-221 genotypes and laboratory data of HCC patients, the only statistical significance was found

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in alpha fetoprotein (AFP) level (P=0.026). It was higher in those with GG

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genotype than in those with GA genotype. Meanwhile, no statistical significance was found regarding Hb, TLC, platelet count, AST, ALT, ALP,

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GGT, total bilirubin and albumin (Table 4).

Regarding miR-101-1 genotypes, we did not find any statistical

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significant difference regarding age, sex, tumor size, portal vein patency, metastasis and ascites. When studying association between miR-101-1 genotypes

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and laboratory data of HCC patients, no statistical significance was found

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regarding Hb, TLC, platelet count, AST, ALT, ALP, GGT, total bilirubin, albumin and AFP.

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Comparing miR-221 genotypes (GA and GG) between HCV patients and control groups, no significant statistical difference were found. As for miR-221 alleles (G and A) distribution between HCV and the control groups no significant statistical difference was found. As regards the distribution of miR-101-1 genotypes (CC, GC and GG) no statistical significant difference were found. Comparing the distribution of miR-101-1 alleles (C and G) between HCV and the control resulted in non-significant statistical difference. 9

ACCEPTED MANUSCRIPT No statistical significance difference was found upon studying the association between miR-221genotypes and the age, sex, liver activity, liver fibrosis or response to interferon treatment among HCV patients. Also no statistical significance was found regarding Hb, TLC, platelet count, AST, ALT, ALP, total bilirubin, albumin, quantitative HCV RNA and AFP.

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We found no statistical significance difference between miR-101-1 genotypes and the age, sex, liver activity, liver fibrosis or regarding response

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to antiviral treatment in HCV group. Also upon studying association analyses between miR-101-1 genotypes and laboratory data of HCV

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patients, no statistical significance was found regarding.

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We didn’t find any statistical significance when comparing HCC and HCV patients regarding miR-221 genotypes or its alleles. As for miR-101-1

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CC and GC genotypes, we got statistical significance (P=0.009 and P=0.037 respectively). For GG genotype of miR-101-1and its alleles, no statistical

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significance was found (Table 3)

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The fold change of miR-221 was significantly up regulated in the sera of HCC and HCV patients. As regards miR-101-1expression, it was

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significantly down-regulated among HCC group but significantly up-

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regulated among HCV patients (Table 5 and figure 1a and 1b).

When studying the correlation between miR-221 and miR-101-1 fold changes and the clinicopathological data of HCC patients, we found no correlation between fold changes and age, Hb, TLC, PLT, AST, ALT, ALP, GGT, Bil T, Albumin, AFP and tumor size. When we analyzed the association between miR-221 genotypes and miR-221 and miR-101-1 fold changes, we didn’t find any significance statistically (P=0.155 and P=0.825 respectively). 10

ACCEPTED MANUSCRIPT Statistically, we got significant value when studying the correlation between miR-221 fold change and miR-101-1 genotypes specifically CC and GG genotypes (Table 6 and figure 2). For the association between miR-1011 genotypes and miR-101-1 fold change, we didn’t get any statistical significance. In table (7), when studying the correlation between both miR-

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221 and miR-101-1 fold change and the demographic and laboratory data of

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change and ALP, Hb, TLC and platelet count.

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HCV patients, we found weak positive correlation between miR-101-1 fold

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In table (8), when we studied the association between miR-101-1 fold change and the response to interferon treatment, we found the association

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was higher among non-responder group compared to responder resulting in statistical significance (Figure 3). As regards miR-221fold change, we didn’t

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find any statistical significance.

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When we analyzed the association between miR-221 genotypes and miR-221 and miR-101-1 fold changes among HCV patients, we didn’t find

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any significance statistically. Also we didn’t find statistical significance

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between miR-101-1 genotypes and its fold change and miR-221 fold change. Figure (4) showed that miR-221 fold change best cutoff value for

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detecting HCC = 2.15 with area under the curve = 0.673, sensitivity = 56.8% and specificity= 73.9%. Figure (5) showed that miR-101-1 fold change best cutoff value for detecting HCC = 0.84 with area under the curve = 0.763, sensitivity = 73% and specificity= 71%. Figure (6) showed that miR-101-1 fold change best cutoff value for detecting response to IFN treatment among HCV patients = 1.453with area under the curve = 0.708, sensitivity = 59.3% and specificity= 89.5%. 11

ACCEPTED MANUSCRIPT Table (9) showed that the only independent predictor of HCC development among HCV patients is miR-101-1 fold change (P=0.002, OR= 0.315). 4. Discussion In Egypt, liver cancer forms 11.75% of the malignancies of all

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digestive organs and 1.68% of the total malignancies (Holah et al., 2015).

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HCC represents the main complication of cirrhosis, and shows a growing

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incidence in Egypt, which may be the result of a shift in the relative importance of HBV and HCV as primary risk factors and improvements in

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screening programs and diagnostic tools (Gomaa et al., 2014). Hepatitis C virus is the major causative agent of HCC, mainly through indirect

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pathways: Chronic inflammation, cell deaths, and proliferation (Koike,

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2007).

Several studies have reported a relationship between miRNAs and

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HCC (Huang et al., 2009). Among several miRNAs implicated in HCC, including miR-221 (Karakatsanis et al., 2013) and miR-101-1 (Huang et

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al., 2016), the aberrant levels of miRNA expressions were upregulated and

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downregulated respectively.

Single nucleotide polymorphisms (SNPs) in some miRNAs and their

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targets have been associated with an increased risk of HCC. Polymorphism in miR-101-1/rs7536540 is positively associated with an increased risk of HCC (Bae et al., 2012). To the best of our knowledge, no previous study has detected the association between polymorphism in miR-221/rs 17084733 and HCC risk. The present work aimed to assess the association between polymorphisms of miR-221 and miR-101-1 and their expressions and the early prediction of HCC in HCV infected patients. 12

ACCEPTED MANUSCRIPT The present study was conducted on thirty seven HCC patients, forty six HCV patients. Also, thirty two healthy controls were included in this study. As regards the laboratory data in our patients, a statistically significant difference (P<0.001) was noted regarding Hb level between HCC and HCV

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patients. This was consistent with the study done by Knight et al. (2004)

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who stated that cancer-related anemia is a common complication in nearly

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all types of malignant diseases including HCC. Another study by Finkelmeier et al. (2014) considered anemia as a risk factor for mortality in

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HCC patients.

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As for platelet count, there was statistically significant difference (P<0.001) between HCC and HCV. This result coincided with the study

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done by Kumada et al. (2010) who revealed that a low platelet count is a predictive factor for the development of HCC. Another study by Carr et al.

thrombocytopenia.

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(2012) stated that only 28 % of the patients with HCC had

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Among HCC patients, AST level was elevated resulting in statistically

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significant difference (P<0.001) versus the HCV group. This agreed with Giannini et al. (2005) who stated that elevated AST level is commonly

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encountered in hepatocellular carcinoma predominance. Our data was also consistent with Wen et al. (2012) who demonstrated that the risk of hepatocellular

carcinoma

increased

exponentially

with

increasing

concentrations of AST. Although the abnormal ALT has been commonly recognized to play an important role, the data Wen et al. (2012) provided showed that the risk conferred by abnormal AST was higher than ALT (6.3 to 8.3-fold increase versus 1.9-fold increase).

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ACCEPTED MANUSCRIPT As for ALT, no statistical significance was detected (P=0.21) and this was consistent with Hann et al. (2012) who did not observe a significant association between ALT and HCC risk in either univariate or multivariate analysis. Our study, regarding AFP, showed statistically significant difference

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between HCC and HCV (P<0.001).This was matched with Kumada et al.

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(2010) who suggested that high AFP levels and low albumin levels are

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associated significantly with HCC development. As for albumin, our work wasn’t consistent with the mentioned study, as despite the statistical

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significance among the studied groups (P<0.001), our HCC patients had got

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low normal albumin level.

For ALP level, there was statistically significant difference between

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HCC versus HCV patients (P<0.001). This agreed with Yu et al. (2011) who related high ALP to poor prognosis in HCC patients.

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Despite being within the normal range, total bilirubin showed statistically significant difference between HCC and HCV (P<0.001). This

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was in contrast to the study done by Carr et al. (2014) who showed an

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association between bilirubin levels and indices of HCC aggressiveness. They also concluded that HCC patients with abnormal bilirubin levels had

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worse prognosis than patients with normal bilirubin. In their total cohort (2416 HCC patients), elevated bilirubin levels were associated with higher AFP levels, increased PVT and multifocality of the tumor. Leucocytes, despite being within the normal range among the studied groups, we got statistically significant difference between HCC and HCV (P=0.002). This was against the study by Huang et al. (2003) who found in Taiwanese adults of both genders that a lower lymphocyte count is associated with cancer mortality, especially mortality from hepatoma. 14

ACCEPTED MANUSCRIPT When we studied the distribution of miR-101-1 CC genotype among the studied groups, we got statistically significant difference between HCC versus control (P=0.006) and HCC versus HCV patients (P=0.009). We didn’t find statistical significance between HCV and the control (P=0.64). As for the distribution of miR-101-1 GC genotype, we found statistically

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significant difference between HCC versus control (P=0.016) and versus

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HCV (P=0.037) but no statistical significance was found between HCV

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versus control (P=0.59).

From these findings, we can conclude that miR-101-1 G>C

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polymorphism is associated with increased HCC risk among Egyptian

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patients. This was coincided with Bae et al. (2012) who found that rs7536540 polymorphism in miR-101-1 is significantly associated with

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development of liver cirrhosis and hepatocellular carcinoma occurrence. This was in contrast to the study done by Pratedrat et al. (2015) who found

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no significant association between miR-101-1 (rs7536540) and the risk of HCC in Thai population.

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Our results also confirmed that allele “C” is associated with increased suggested

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HCC risk. This was against the study by Li et al. (2009) who

that the variant allele “C” of rs7536540 may have a protective effect on the

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development of liver cirrhosis and HCC in chronic hepatitis patients. We didn’t find statistical significance when comparing miR221genotypes [GA and GG] and alleles’ distribution among the studied groups. So, we suggested that miR-221 G>A polymorphism is not significantly associated with the risk of HCC development among Egyptian patients. To the best of our knowledge, no previous studies investigated this polymorphism in HCC patients. It is noteworthy that miR-221 G>A polymorphism had been associated with increased risk of other cancers like 15

ACCEPTED MANUSCRIPT papillary thyroid carcinoma (He et al., 2005) and acral melanoma (Godshalk et al., 2011). We investigated miR-221 fold change among the studied groups. We found that miR-221 was upregulated by 3.08 fold among HCC and by 1.24 fold in HCV patients resulting in statistically significant difference

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(P=0.007).

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Similar to the present data, it was found that among all the HCC-

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related miRNAs, miR-221 was reported to be increasingly expressed in HCC (Fornari et al., 2008). Another study by Ji et al. (2009) reported thatmiR-

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221 was found to be over-expressed in HCC and to dys-regulate important

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cancer pathways. In the study by Thurnherr et al. (2016), miR-221was found to be moderately correlated in expression in HCC patients. It is to be that

miR-221

contributed

to

hepatocarcinogenesis

by

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dysregulating DNA damage-inducible transcript 4 and targeting the Bmf

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gene relevant to apoptosis in HCC (Pineau et al., 2010). The overexpression of miR-221 was associated with cell migration in HCC through enhanced

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AKT signaling (Wong et al., 2010).

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In contrast to our results, the data presented by El-Garem et al. (2014) showed significant fold decrease in serum miR-221 in HCC group (0.92) in

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comparison to normal control. They assumed that with the progression of liver disease from chronic hepatitis to cirrhosis, the increased activity of hepatic stellate cells was associated with increase miR-221 expression level, such high level stimulated tumorigenesis and increase level of miR-221 in tissue, but as miR-221 is anti-apoptotic so serum miR-221 didn’t show similar increase. According to Diaz et al. (2013) miR-221 was implicated in the progression of hepatitis virus associated HCC and in response to virus 16

ACCEPTED MANUSCRIPT infection (Marquez et al., 2010). Another study by Gang et al. (2014) found that miR-221 was upregulated in serum of HCV chronic hepatitis patients and functioned as an IFN enhancer in HCV infection. As for miR-101-1 fold change among the studied groups, it was downregulated in HCC group by 0.56 fold and among HCV patients, it was

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upregulated by 2.04 fold resulting in statistically significant difference

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(P<0.001).

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Coinciding with our data, downregulation of miRNA-101-1 in association with HCC was also reported in different studies that have

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collectively shown that miRNA-101-1 expression has the potential to inhibit

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HCC cell proliferation, suppress tumorigenicity and promote apoptosis by modulating the expression of multiple transcription factors and cell cycle-

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related genes in HCC [Xu et al. (2014) and wang et al. (2014)].Another study by Budhu et al. (2008) showed that reduced expression of miR-101 is

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associated with worse survival of HCC patients. To the best of our knowledge, no previous study has detected the

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upregulation of miR-101-1 expression in HCV patients. From the present

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data, we suggested that serum miR-101-1 level can serve as a potential noninvasive biomarker to differentiate HCV associated HCC from HCV.

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A relatively similar study was done but included HBV patients in addition to HCC group. This study by (Xie et al., 2014) showed that serum miR-101-1 was significantly downregulated in HBV-HCC patients compared with its significant upregulation in HBV patients (P< 0.001). In the present study, we didn’t find any correlation between miR-221 fold change and tumor size in HCC patients (P=0.41).This was in contrast to the study done by Fu et al. (2011) who found that enhanced miR-221 expression in HCC tissue was associated with tumor size (P = 0.008). They 17

ACCEPTED MANUSCRIPT assumed that miR-221 has many other target genes and some of them may affect the tumor size. As for miR-101-1 fold change and tumor size, we didn’t also find any correlation (P=0.1). In a little bit similar study but included breast cancer patients, no significant correlation (P=0.4) was found between miR-101

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expression and tumor size (Liu et al., 2015).

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As for the correlation between miR-101-1 fold change and

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demographic and laboratory data of HCV group, we found weak positive correlation regarding the ALP level (P=0.031). In contrast to our result, the

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study by Mizuno et al. (2008) revealed thatmiR-125b, with its anti-

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proliferative action like miR-101-1, over-expression decreases ALP activity. We also found weak positive correlation between miR-101-1fold

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change and Hb level (P=0.002). This could be explained by the fact that human mature erythrocytes contain diverse and abundant miRNAs (Chen et

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al., 2008c) and the increased expression of these miRNAs in primary erythroid progenitor cells results in elevated fetal and embryonic

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hemoglobin gene expression (Sankaran et al., 2011).

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We were interested in studying the association between response to interferon treatment (IFN) and the studied miRNAs fold changes among

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HCV patients. Regarding miR-101-1 fold change, we got statistically significant difference between responders versus those who didn’t respond to treatment (P=0.017). miR-101-1 was upregulated by 2.56 fold in nonresponders more than responders where it was upregulated by only 1.24 fold. When we made the ROC curve to detect the response to IFN treatment in HCV group, the best cutoff value was 1.45 (AUC=0.708, sensitivity = 59.3% and specificity= 89.5%). MiR-101-1 fold change above this cutoff value indicates that the patient won’t respond to therapy. This result was 18

ACCEPTED MANUSCRIPT coincided with the study done by Murakami et al. (2010) where the difference in the mean values of the miR-101 gene expression level between responders and non-responders was 1.31 fold (p < 0.05). When we made ROC curve for prediction of HCC using the fold change of miR-221, it revealed that the best cutoff value was 2.15

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(AUC=0.67, sensitivity=56.8% and specificity=73.9%). So, the fold changes

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of miR-221 above this cutoff value are most probably predictive of HCC.

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This agreed with the study done by El-Garem et al. (2014) which revealed that ROC curve analysis for miR-221 yielded 87% sensitivity and 40%

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specificity for the differentiation of HCC patients from non-HCC at a cutoff

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1.82. This led them to the conclusion that serum miR-221 has a strong potential to serve as one of the novel non-invasive biomarkers of HCC.

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As for the ROC curve for prediction of HCC using the fold change of miR-101-1, it showed that the best cutoff value was 0.84 (AUC=0.76,

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sensitivity=73% and specificity=71%). From this data, miR-101-1 fold changes below this cutoff value are mostly predictive of HCC. This was

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consistent with the study done by Zhuang et al. (2015) where ROC curve

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analysis revealed that miR-101 could differentiate HCC patients from healthy controls. The same study group showed that miR-101 had also

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diagnostic potential for differentiating HCC from chronic hepatitis (CH) with AUC of 0.623 (54.9% sensitivity and 76.9% specificity). Finally, we made the multivariate logistic regression analysis to detect independent HCC development predictors. miR-101-1 fold change was found to be the only predictor. 5. Conclusion

19

ACCEPTED MANUSCRIPT In our study, we confirmed that miR-101-1 (rs 7536540) polymorphism is associated with increased risk of HCC development among Egyptian patients. Accordingly, we can declare that variant allele “C” is associated with significantly increased occurrence of HCC. As for miR-221 (rs 17084733) polymorphism, our results suggested it is not associated with

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increased risk of HCC. We proved throughout this study that both miR-221

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and miR-101 -1 expression levels can be used as non invasive diagnostic

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biomarkers for HCC. Using the ROC curve, it revealed that the best cutoff value of miR-221 fold change was 2.15 (AUC=0.67, sensitivity=56.8% and

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specificity=73.9%). So, the fold changes of miR-221 above this cutoff value

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are most probably predictive of HCC. As for the ROC curve for prediction of HCC using the fold change of miR-101-1, it showed that the best cutoff

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value was 0.84 (AUC=0.76, sensitivity=73% and specificity=71%). From this data, we concluded that miR-101-1 fold changes below this cutoff value

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are mostly predictive of HCC. The possibility of using miR-101-1 fold

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change to detect the HCV patient’s response to IFN treatment.

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ACCEPTED MANUSCRIPT References  Bae JS, Kim JH, Pasaje CF, Cheong HS et al. (2012): Association study of genetic variations in microRNAs with the risk of hepatitis Brelated liver diseases. Dig. Liver Dis.; 44(10):849-854.

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 Bartel DP.(2004): MicroRNAS: genomics, biogenesis, mechanism and

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function. Cell; 116: 281-297.

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 Chan YH (2003a): Biostatistics102: Quantitative Data – Parametric &

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ACCEPTED MANUSCRIPT  Diaz G, Melis M, Tice A, Kleiner DE et al. (2013): Identification of microRNAs specifically expressed in hepatitis C virus-associated hepatocellular carcinoma. Int. J. Cancer; 133:816–824.  El-Garem H, Ammer A, Shehab H, Shaker O et al. (2014): Circulating microRNA, miR-122 and miR-221 signature in Egyptian

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 Finkelmeier F, Bettinger D, Köberle V, Schultheiß M et al. (2014): Single measurement of hemoglobin predicts outcome of HCC patients.

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hepatocellular carcinoma. Oncogene; 27(43):5651-5661.  Fu X, Wang Q, Chen J, Huang X et al. (2011): Clinical significance of

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miR-221 and its inverse correlation with p27Kip¹ in hepatocellular

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ACCEPTED MANUSCRIPT  Gomaa AI, Hashim MS and Waked I. (2014): Comparing staging systems for predicting prognosis and survival in patients with hepatocellular carcinoma in Egypt. PLoS One; 9: e90929.  Gramantieri L, Fornari F, Callegari E, Sabbioni S et al. (2008): MicroRNA involvement in hepatocellular carcinoma. J. Cell Mol. Med.;

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ACCEPTED MANUSCRIPT  Huang ZS, Chien KL, Yang CY, Wang CH et al. (2003): Peripheral differential leukocyte counts and subsequent mortality from all diseases, cancers, and cardiovascular diseases in Taiwanese. J. Formos. Med. Assoc.; 102(11):775-81.  Jemal A, Bray F, Center M, Ferlay J et al. (2011): Global cancer

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al. (2013): Expression of microRNAs, miR-21, miR-31, miR-122, miR145, miR-146a, miR 200c, miR-221, miR-222, and miR-223 in patients

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expression profiles classify human cancers. Nature; 435: 834–838.

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parameters associated with chronic hepatitis C viral infection in humans. Lab. Invest.; 90(12):1727-1736.

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miR-125b inhibits osteoblastic differentiation by down-regulation of cell proliferation. Biochem. Biophys. Res. Commun.; 368(2):267-272.

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treatment of chronic hepatitis C. BMC Medical Genomics; 3: 48-60.  Pineau P, Volinia S, McJunkin K, Marchio A et al. (2010): miR-221 overexpression contributes to liver tumorigenesis. Proc. Natl. Acad. Sci. USA; 107(1):264-269.  Pratedrat P, Sopipong W, Jarika Makkoch J, Praianantathavorn K et al. (2015): Single Nucleotide Polymorphisms in miR-149 (rs2292832) and miR-101-1 (rs7536540) are not associated with Hepatocellular Carcinoma in 25

ACCEPTED MANUSCRIPT Thai Patients with Hepatitis B Virus Infection. Asian Pac. J. Cancer Prev.; 16 (15): 6457-6461.  Ryan BM, Robles AI and Harris CC. (2010): Genetic variation in microRNA networks: the implications for cancer research. Nat. Rev. Cancer; 10: 389–402.

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 Sankaran VG, Menne TF, Scepanovic D, Vergillo JA et al. (2011): MicroRNA-15a and -16-1 act via MYB to elevate fetal hemoglobin

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expression in human trisomy 13. Proc. Natl. Acad. Sci.; 108:1519–1524.  Sanyal AJ, Yoon SK and Lencioni R. (2010): The etiology of carcinoma

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Epidemiological characteristics of hepatocellular carcinoma in Egypt: a retrospective analysis of 1313 cases. Liver Int.; 33(10):1601-1606.

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genetic and epigenetic alterations in hepatocellular carcinoma from Southeast China. Mutat. Res.; 641:27–35.

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Genetic Information Processing and Metabolism Pathways.Sci. Rep.; 6: 20065.

 Wang L, Zhang X, Jia LT, Hu SJ et al. (2014): c-Myc mediated epigenetic silencing of microRNA-101 contributes to dysregulation of multiple pathways in hepatocellular carcinoma. Hepatology; 59: 1850– 1863.

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ACCEPTED MANUSCRIPT  Wen CP, Lin J, Yang YC, Tsai MK et al. (2012): Hepatocellular Carcinoma Risk Prediction Model for the General Population: The Predictive Power of Transaminases. J. Natl. Cancer Inst.; 104 (20):15991611.  Wong QW, Ching AK, Chan AW, Choy KW et al. (2010): MiR-222

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overexpression confers cell migratory advantages in hepatocellular carcinoma through enhancing AKT signaling. Clin. Cancer Res.; 16:

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867–875.

 Xie Y, Yao Q, Butt AM, Guo J et al. (2014): Expression profiling of

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serum microRNA-101 in HBV-associated chronic hepatitis, liver

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cirrhosis, and hepatocellular carcinoma. Cancer Biol. Ther.; 15(9): 12481255.

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 Xu L, Beckebaum S, Iacob S, Wu G, et al. (2014): MicroRNA-101 inhibits human hepatocellular carcinoma progression through EZH2

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downregulation and increased cytostatic drug sensitivity. J. Hepatol.; 60: 590-598.

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 Yu MC, Chan KM, Lee CF, Lee YS et al. (2011): Alkaline

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phosphatase: does it have a role in predicting hepatocellular carcinoma recurrence? J. Gastrointest. Surg.; 15(8): 1440-1449.

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 Zhuang C, Jiang W, Huang D, Xu L et al. (2015): Serum miR-21, miR-26a and miR-101 as potential biomarkers of hepatocellular carcinoma. Clin. Res. Hepatol. Gastroenterol.; S2210-7401(15): 263-266.

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Figure (1): a) Comparative analysis between HCC &HCV regarding miR-221 fold change.

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b) Comparative analysis between HCC & HCV regarding miR-101-1 fold change.

29

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Figure (2): Association between miR-101-1 genotypes and miR-221 fold change in

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HCC.

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Figure (3): Association between response to interferon treatment in HCV patients and

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miR-101-1 fold change.

31

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Figure (4): ROC curve for prediction of HCC using fold change of miR-221.

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Figure (5): ROC curve for prediction of HCC using fold change of miR-101-1.

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Figure (6): ROC curve for prediction of response to IFN treatment using fold change of

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miR-101-1 in HCV patients.

34

ACCEPTED MANUSCRIPT Table (1a): Clinicopathological parameters of HCC patients. Tumor size

19

51.4%

18

48.6%

Yes

2

5.4%

PV

NO

35

94.6%

Thrombosed

9

24.3%

Patent

28

75.7%

Yes

1

NO

36

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IHBR

METASTASIS

NO

97.3%

13

35.1%

24

64.9%

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Yes ASCITES

2.7%

IP

Liver (US)

< 5cm ≥ 5cm

Count

%

19

42.2%

A2

16

35.6%

A3

10

22.2%

F1

21

46.7%

F2

16

35.6%

F3

8

17.8%

responder

27

58.7%

non-responder

19

41.3%

A1

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M

Liver Activity

Liver Fibrosis

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CE

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Response to interferon treatment

HCV

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Table (1b): Clinicopathological parameters of HCV patients.

35

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Table (2): Comparison between laboratory data of HCC and HCV patients. HCC

HCV

Minimum

Maximum

Median

Minimum

Maximum

Hb (g/dL)

11.80

5.50

16.60

14.00

9.50

16.40

WBCs (*1000/mm3)

5.50

1.90

11.00

6.70

3.10

11.50

PLT (*1000/mm3)

98.00

9.00

400.00

221.00

100.00

AST (U/L)

99.00

31.00

287.00

58.50

ALT (U/L)

65.00

20.00

223.00

60.00

ALP(U/L)

160.00

20.00

1010.00

BIL T (mg/dl)

1.45

0.30

Albumin (g/dL)

3.50

AFP (ng/mL)

201.00

IP

T

Median

424.00

P value <0.001* 0.002* <0.001*

104.00

<0.001*

19.00

117.00

0.214

89.50

38.00

290.00

<0.001*

10.70

0.70

0.30

1.40

<0.001*

2.10

7.00

4.00

3.30

5.10

<0.001*

1.20

29948.00

3.70

0.60

17.90

<0.001*

AC

CE

PT

ED

M

AN

US

CR

17.00

36

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Table (3): Comparative distribution of miR-221 and miR-101-1 genotypes and alleles among HCC, HCV patients and the control group.

Count

%

HCV (46) Count

%

Control (32) Count

%

P1

OR1 (95%

P2

value

CI)

value

2.667 8

21.6%

8

17.4%

3

9.4%

miR-221

G

Alleles

Allele

78.4%

38

82.6%

29

90.6%

66

89.2%

84

91.3%

61

95.3%

0.375 (0.09-

8.7%

CC

10

27.8%

3

6.5%

miR-101-1 GC

12

33.3%

Allele C

PT

14

38.9%

CE

GG

AC

Allele

32

G

40

26

44.4%

55.6%

17

32

AN

8

Genotypes

3

4.7%

M

10.8%

1

3.1% 0.006*

20

62.5% 0.016*

ED

8

A

56.5%

37.0%

0.646

0.786 (0.2802.204)

0.009*

5.513 (1.38821.89)

0.037*

0.385 (0.1560.951)

0.858

1.086 (0.4422.666)

0.208

1.5 (0.7972.824)

11.923(1.4399.4) 0.3 (0.1110.812) 1.215

11

34.4%

0.7

(0.4513.271)

34.8%

22

34.4%

1.572 0.231

60

0.406

(0.103-1.6)

65.2%

1.31 (0.4393.908)

0.763 (0.2562.276)

1.556)

0.185

OR2 (95% CI)

0.627

CR

Allele

29

US

GG

Alleles

11.068)

0.166

Genotypes

miR-101-1

(0.642-

IP

GA miR-221

T

HCC (37)

42

1 HCC VS control 2 HCC VS HCV

37

65.6%

(0.7633.058)

ACCEPTED MANUSCRIPT

Table (4): Association between miR-221 genotypes and laboratory data of HCC patients. miR-221 Genotypes GA

GG P value

Minimum

Maximum

Median

Minimum

Maximum

Hb (g/dL)

10.40

7.30

13.50

12.50

5.50

16.60

0.145

TLC *1000/mm3

5.35

1.90

8.10

5.70

2.30

11.00

0.796

PLT*1000/mm3

77.50

54.00

195.00

122.00

9.00

400.00

0.366

AST (U/L)

59.00

39.00

223.00

108.00

31.00

287.00

0.31

ALT (U/L)

37.50

20.00

223.00

66.00

23.00

211.00

0.268

ALP (U/L)

144.50

95.00

209.00

162.00

20.00

1010.00

0.209

GGT (U/L)

40.00

30.00

173.00

56.00

17.00

343.00

0.245

BIL T (mg/dl)

1.20

0.80

1.80

1.60

0.30

10.70

0.26

BIL D (mg/dl)

0.50

0.00

1.20

0.45

0.01

7.60

0.825

ALBUMIN (g/dL)

3.45

2.70

7.00

3.50

2.10

7.00

0.579

AFP (ng/mL)

17.00

485.00

285.00

6.10

29948.00

0.026*

38

IP

CR

US

AN

M

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AC

CE

PT

1.20

T

Median

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Table (5): Comparative analysis between HCC and HCV regarding miR221 and miR-101-1 expression. HCC

HCV P value

Fold change of

0.08

11.43

1.24

0.00

0.56

0.00

2.89

2.04

0.03

AC

CE

PT

ED

M

AN

US

miR-101-1

3.08

39

23.84

IP

miR-221

21.74

CR

Fold change of

T

Median Minimum Maximum Median Minimum Maximum 0.007*

<0.001*

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Table (6): Association between miR-101-1 genotypes and miR-221 and miR-101-1 fold changes in HCC. miR-101-1 genotypes CC

GC

GG

4.71

2.76

7.64

2.75

.43

8.91

1.08

0.08

11.43

0.021*

Fold change of miR-1011

0.63

0.38

2.77

0.47

0.02

2.89

0.00

1.07

0.064

0.35

US

-P value between CC and GC= 0.121

IP

Fold change of miR-221

CR

T

Media Minim Maxim Media Minim Maximu Media Minimu Maximu P value n um um n um m n m m

-P value between CC and GG= 0. 008*

AC

CE

PT

ED

M

AN

-P value between GC and GG= 0.147

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ACCEPTED MANUSCRIPT Table (7): Correlation between fold changes of miR-221 and miR-101-1 and

Fold change of miR-221

Fold change of miR-101-1

r

-0.279

-0.063

P value

0.061

0.676

r

-0.089

-0.194

P value

0.556

r

-0.111

P value

0.464

r

0.278

P value

0.061

r

0.002

0.280

P value

US

0.031*

AN

demographic and laboratory data of HCV group.

0.988

0.059

0.192

-0.043

0.201

0.775

0.114

0.190

P value

0.449

-0.206

r

-0.084

-0.085

P value

0.579

0.576

r

0.047

0.438

P value

0.756

0.002*

r

0.169

-0.296

P value

0.261

0.045*

r

-0.102

-0.371

P value

0.498

0.011*

T

Age (years)

ALP(U/L)

BIL T (mg/dl) r Albumin (g/dL) r

ED

AFP (ng/mL)

CE

PT

HCV RNA Quant

Hb (g/dL)

AC

TLCsx1000/mm3

PLTs x1000/mm3

M

P value

41

CR

ALT((U/L)

IP

AST (U/L)

0.197

-0.055 0.716 0.318

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Table (8): Association between response to interferon treatment in HCV patients and miR-221 and miR-101-1 fold changes. Response to ttt responder

non-responder P value

Fold change of

Minimum

1.19

0.00

6.02

1.24

0.02

1.24

0.03

9.40

2.56

AN M ED PT CE AC

42

Maximum

T

Median

US

miR-101-1

Maximum

IP

miR-221

Minimum

CR

Fold change of

Median

0.48

23.84

0.973

21.74

0.017*

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Table (9): Multivariate logistic regression analysis to detect independent predictors of HCC 95.0% C.I. P value

OR

1.156

Fold change of miR-101-1

0.002*

0.315

miR-101-1 genotypes

0.058

Genotype CC

0.149

Genotype GC

0.200

1.355

0.659

3.647

0.630

21.130

0.450

0.133

1.524

US

0.150

AN

M ED PT CE AC

43

0.986

IP

0.073

CR

Fold change of miR-221

Upper

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Lower

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- HCV is one of most common viral infection in Egypt that can lead to HCC - Both miR-221 and miR-101 -1 expression levels may be useful as non invasive diagnostic biomarkers for early prediction of HCC among HCV patients. - As for the ROC curve for prediction of HCC using the fold change of miR-101-1, it showed that the best cutoff value was 0.84 (AUC=0.76, sensitivity=73% and specificity=71%). From this data, we concluded that miR-101-1 fold changes below this cutoff value are mostly predictive of HCC.

44