Diagnosis and staging of HCV associated fibrosis,cirrhosis and hepatocellular carcinoma with target identification for miR-650,552-3p,676-3p,512-5p and 147b

Abstract

BACKGROUND:

Chronic HCV infection progresses to fibrosis, cirrhosis and hepatocellular carcinoma (HCC). The latter represents the third most common cause for cancer mortality. Currently, there is no reliable non-invasive biomarker for diagnosis of HCV mediated disorders.

OBJECTIVE:

Profiling expression signature for circulatory miRNAs in the plasma of 167 Egyptian patients (40 healthy, 48 HCV fibrotic, 39 HCV cirrhotic and 40 HCV-HCC cases).

METHODS:

QRTPCR was used to quantify expression signature for circulatory miRNAs.

RESULTS:

MiR-676 and miR-650 were powerful in discriminating cirrhotic and late fibrosis from HCC. MiR-650 could distinguish mild (f0-f1) and advanced (f2-f3) fibrosis from HCC cases. MiR-650 and miR-147b could distinguish early fibrosis from healthy controls meanwhile miR-676 and miR-147b could effectively distinguish between mild chronic and (f1-f3) cases from healthy individuals. All studied miRNAs, except miR-512, can differentiate between (f0-f3) cases and healthy controls. Multivariate logistic regression revealed three potential miRNA panels for effective differentiation of HCC, cirrhotic and chronic liver cases. MiR-676-3p and miR-512-5p were significantly correlated in (f1-f3) fibrosis meanwhile miR-676 and miR-512 could differentiate between cirrhosis and (f0-f3) cases. Both miR-650 and miR-512-5p were positively correlated in the cirrhotic group and in (f0-f4) group. Putative targets for investigated miRNAs were also determined.

CONCLUSIONS:

Investigated miRNAs could assist in staging and diagnosis of HCV associated disorders.

Keywords

Biomarkers HCV miRNA HCC fibrosis

1. Introduction

Hepatitis C virus (HCV) infection is a contagious disorder with an estimated 185 million infected cases globally [1]. The highest prevalence rates are found in Eastern Mediterranean region followed by European and the African regions [2]. Egypt has the highest HCV prevalence rate worldwide where 14.7% of its population is infected with HCV genotype 4 [3]. Approximately 70% of individuals infected with HCV develop chronic HCV (CHC) which could end up with more devastating disorders such as fibrosis, irreversible cirrhosis and hepatocellular carcinoma (HCC) [2].

Liver fibrosis is a wound-healing response to chronic liver injury [4]. Persistence of fibrosis could lead to hepatic scar formation and irreversible cirrhosis that might ultimately lead to HCC progression [5, 6]. HCC is the fifth most common cancer and the world’s third leading cause of cancer death [7]. HCV associated HCC is the leading cause of death for about one million cases annually [8]. Nearly 31% of HCC cases arise from HCV infection [9]. The incidence of HCC is expected to increase in the next two decades, primarily due to HCV infection and secondary owing to development of cirrhotic stages [10].

Despite recent development of successful strategies against HCV, death rate arising from HCV associated disorders is still high [11]. Therefore, early detection of HCV mediated disease progression is very crucial. Although liver biopsy is the gold standard approach for this purpose, it is not widely implemented as it is expensive, invasive with the possibility for sampling error in addition to inter- and intra-observer variation in pathology reporting [12]. Few non-invasive hepatic markers were developed such as fibrosis-4 FIB-4, AST/platelet ratio index (APRI) and Göteborg University Cirrhosis Index (GUCI) [13]. Alpha feto-protein (AFP) and des-gamma-carboxy prothombin (DCP) are the main non-invasive biomarkers used for HCC diagnosis. However, all those biomarkers can not accurately distinguish between various stages of HCV mediated liver disorders and lack sensitivity [5, 14, 15]. There is urgent need for investigating other reliable non-invasive biomarkers for early staging, diagnosis and/or prognosis.

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene silencing by targeting mRNA [10, 16]. Aberrant expression of miRNAs has been linked to variety of cancer diseases [17, 18]. Circulating miRNAs leak out of cells into body fluids such as plasma and urine [19, 20]. They were reported to be stable against endogenous RNases and to display consistent profiles among healthy individuals [21, 22]. These characteristics established their potential value as biomarkers during pathological changes [23]. For instance, miR-25 and miR-223 were shown to be reliable serum biomarkers for lung cancer [24] while miR-122 was reported as a promising biomarker in HCC cases [23]. Moreover, miR-184 and miR-92a were reported for squamous cell carcinoma and leukemia detection respectively [25, 26]. Furthermore, miR-141 and miR-375 were the most promising biomarkers correlated with prostate tumor progression [27].

The main objective of the current study is to investigate expression profiles for 5 plasma miRNAs namely; miR-650, miR-512-5p, miR-552-3p, miR-147b and miR-676-3p in various stages of HCV associated hepatic disorders among Egyptian patients in an attempt to identify potential non-invasive biomarkers for early diagnosis and staging purposes.

Rationale for selecting the five miRNAs in this study and their role in liver pathogenesis:

It was reported that miR-512 promoted oncogenic effects by targeting two members of the downstream tumor suppressors of JNK axis namely MAP3K2 and MAP2K4. The latter were believed to be significantly down-regulated in hepatocellular carcinoma (HCC) [28]. Concerning miR-650, it was postulated to promote migration, invasion, and Epithelial-Mesenchymal Transition (EMT) of HCC cells by targeting the large tumor suppressor kinase 2 gene (LATS2) [29]. It is also believed that it inhibits expression of the tumor suppressor gene namely inhibitor of growth protein 4 (ING4) and causes the progression of HCC [30]. ING4 is an emerged multimodal tumor suppressor gene of ING family where its expression is downregulated or even completely lost in various human cancers including HCC. Moreover, low expression of ING4 in liver cancerous tissues for patients with HCC is significantly correlated with rapid cancer progression and patient poor prognosis. Based on these facts, ING4 is being considered as a potential candidate in the gene-based cancer therapy approach [31]. In vitro studies indicated that miR-552 promotes HCC progression by blocking WIF1mediated GSK3 $\beta$ dephosphorylation [32]. Studies have shown that GSK-3 $\beta$ is involved in HSC activation, transdifferentiation, and subsequent proliferative processes during liver fibrosis [33]. Moreover, down-regulating miR-552 expression level inhibits cell migration and invasion, and promotes cell apoptosis via the tumor suppressor gene RUNX3 (Runt-related transcription factor 3) [34]. The presence of hypermethylated RUNX3 is linked with HCC in patients with HCV-related cirrhosis and was used as a biomarker to distinguish HCC cases from cirrhotic groups [35]. It also promotes epithelial-mesenchymal transition in various cancer types including HCC by inhibiting the tumor suppressor gene AJAP1 (Adherent junction associated protein 1) [36]. Concerning miR-147b, It was reported to promote tumor growth via targeting UBE2N ubiquitin-conjugating enzyme in HCC [37]. Limited studies are available for miR-676. It was recently identified as a target for p53 which regulates cell cycle and functions as a tumor suppressor gene [38] as it provides protection against development of fibrosis. Following the death of the damaged hepatocytes, the quiescent hepatocyte stellate cells (HSC) are activated to regenerate fibrotic scars by wound healing. An uncontrolled continuous wound healing process may produce a fibrotic liver, paving the way for the development of more severe liver diseases such as cirrhosis and even HCC. Recently, it was found that p53 attenuates the fibrotic process by inducing HSC senescence in a non-cell autonomous mechanism. Thus, through its activity in HSCs, p53 balances the fibrosis response and prevents the potential progression of fibrosis to HCC. Furthermore, p53 was suggested to limit HCC progression [39].

The expression levels for miR-650, miR-512-5p, miR-552-3p, miR-147b and miR-676-3p were not profiled previously in the plasma of HCV mediated cirrhosis, fibrosis and HCC patients across the globe in general and among Egyptians in particular. Therefore, this study aimed at investigating actual expression signature for those 5 miRNAs in the plasma of Egyptian patients affected with various stages of HCV associated hepatic disorders including fibrosis, cirrhosis and HCC. Our study also provided further insights and deep analysis for the possible mechanistic pathways and targets triggered by those 5 miRNAs.

2. Materials and methods

2.1 Subjects

This study included 127 CHC Egyptian patients (26 mild fibrosis and 22 advanced fibrosis, 39 liver cirrhotic and 40 HCC subjects) with HCV viral load as measured by qRT-PCR (Step-One Plus, Applied Biosystems, USA). A written informed consent was obtained from all participants and the study protocol was approved by ethics review committee at Cairo University (Serial: BC2116) and was conducted following Helsinki declaration. Inclusion criteria set for this study were; age $>$ 18 years old with HCV infection. Patients with chronic hepatitis or cirrhosis arising from non HCV and patient with any other malignancy other than HCC were excluded from this study. Cirrhotic and fibrotic patients were confirmed using FibroScan scores. HCC diagnosis was confirmed by multiphasic magnetic resonance imaging (MRI) and/or dynamic computed tomography (CT) with measurements of AFP levels. For normalization purposes, forty healthy volunteers (negative anti-HCV test and viral load) were also recruited.

2.2 Routine laboratory tests, RNA extraction and cDNA synthesis

Blood samples were collected and the following biochemical tests were performed; RBCs count, CBC, Hb, total leukocyte count, platelet count, prothrombin time, MCV, INR, AFP, ALP, albumin, ALT, AST and total bilirubin as reported previously [40, 41, 42]. Total RNA was extracted using Direct-zol™RNA MiniPrep Kit (Zymo Research, USA) according to manufacturer’s protocol. Prior to the ethanol precipitation step, cel-miR-39-3p (Qiagen, Germany) was used as a spike in control for all samples. Extracted RNA samples were quantified using Nanodrop Q5000 UV-Vis (Quawell, USA) [43, 44, 45] and then 100 ng RNA sample was poly-adenylated using E. coli Poly(A) Polymerase (New England Biolabs, UK). This was followed by reverse transcription using the GoScript Reverse Transcription kit (Promega, USA) according to manufacturer’s manual.

2.3 Quantitative Real-time PCR (qRT-PCR)

QRT-PCR was carried out using the GoTaq ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}{\textregistered}}$ qPCR Master Mix (Promega, USA, Cat No A6001) using the StepOne Plus (Applied Biosystems, USA). To ensure accurate quantitation for investigated miRNAs and to avoid non-specific amplification for the designed primers, melt curve analysis was conducted after each PCR run. The 2 ${}^{-\Delta\Delta\text{Ct}}$ formula was used to determine relative miRNA expression and fold change as reported previously [46, 47, 48, 49, 50]. The primers used are shown in (Table 1).

Table 1
Primers sequences used for amplification in qRT-PCR

Primer	Sequence
miR-676-3p	F: 5’-CAGCTGTCCTAAGGTTGTTG-3’
	R: 5’-GGTCCAGTTTTTTTTTTTTTTTAACTC-3’
miR-512-5p	F: 5’-GCAGCACTCAGCCTTGA-3’
	R: 5’-GGTCCAGTTTTTTTTTTTTTTTGAAAG-3’
miR-650	F: 5’-GAGGCAGCGCTCTCA-3’
	R: 5’-GGTCCAGTTTTTTTTTTTTTTTGTC-3’
miR-552-3p	F: 5’-GCAGAACAGGTGACTGGT-3’
	R: 5’-GGTCCAGTTTTTTTTTTTTTTTGTCTA-3’
miR-147b	F: 5’-GTGTGCGGAAATGCTTC-3’
	R: 5’-GTCCAGTTTTTTTTTTTTTTTAGCAG-3’
miR-39-3p	F: 5’-GTCACCGGGTGTAAATCAG-3’
	R: 5’-GGTCCAGTTTTTTTTTTTTTTTCAAG-3’

2.4 Statistical analysis and target prediction

The Data analysis was performed using SPSS software version-15 and GraphPad Prism-5.0). Data are represented as mean $\pm$ SEM. One Way ANOVA, Tukey, post hoc and Chi square tests were applied for comparison with $P$ value $\leqslant$ 0.05 considered to be significant. For non-parametric analysis, Mann-Whitney and Kruskal-Wallis tests were implemented for dual and multiple comparisons respectively and data was presented using median and quartiles. Spearman correlation analysis was conducted to investigate correlation between selected miRNAs in each category. The correlation between the studied miRNAs and the clinical data was analyzed using pearson correlation. Receiver operator characteristic (ROC) curves were constructed and area under the curve (AUC) was calculated to determine diagnostic capability of the studied miRNAs in discriminating between the investigated stages. TargetScan and miRDB prediction programs were used to determine the putative targets for the chosen miRNAs to explain their expression dysregulation in the context of HCV related diseases.

3. Results

3.1 Demographic and clinical features of healthy control, CLD (F0-F3), cirrhotic and HCC patients

Healthy controls had negative HCV viral load while all other participating groups showed positive HCV viral load with comparable viral titer Supplementary Table 1. Hepatic enzymes (AST, ALT and ALP) and bilirubin levels were significantly upregulated among all diseased groups ( $P<$ 0.0001) with an increasing trend towards late disease progression. Albumin synthetic function, platelet count, RBCs count and hemoglobin concentration decreased significantly ( $P<$ 0.0001) in all diseased groups particularly in cirrhotic and HCC patients. On the other side, prothrombin coagulation time, urea level and total leukocyte count increased significantly ( $P=$ 0.0203, $P<$ 0.0001 and $P<$ 0.0001 respectively) with liver disease progression especially in cirrhotic and HCC groups. AFP levels exceeded normal values in HCC group only but not in remaining groups. Clinical data provided clearly show perfect categorization of the participating subjects of this study with proper group assignment for each disease stage.

3.2 Differential expression of the investigated miRNAs among the studied groups

Kruskal-Wallis multiple comparisons revealed significant differentiation for all investigated miRNAs among all the participating groups (Fig. 1). Comparing HCC to healthy controls using Mann-Whitney showed that both miR-676-3p and miR-512-5p were significantly upregulated in HCC group ( $P=$ 0.0023 and $P=$ 0.0297 respectively) meanwhile miR-650 and miR-552-3p were significantly down-regulated ( $P<$ 0.0001). Comparing CLD group (f0-f3) to healthy subjects revealed that miR-676-3p, miR-650 and miR-552-3p were significantly upregulated in CLD patients ( $P=$ 0.0030, $P=$ 0.0346 and $P=$ 0.0218 respectively) meanwhile miR-147b decreased significantly ( $P=$ 0.0002) (Fig. 1). Comparing cirrhotic group (f4) to controls revealed remarkable upregulation in expression profiles for miR-676-3p and miR-552-3p in cirrhotic patients ( $P=$ 0.0004 and $P=$ 0.0008 respectively) with significant downregulation for miR-512-5P and miR-147b ( $P=$ 0.0004 and $P<$ 0.0001 respectively) (Fig. 1).

Figure 1.

Differential expression of plasma miRNA levels in Healthy Control, CLD, cirrhotic and HCC patients. The box represents the 25%–75% percentiles; the line inside the box represents the median and the upper and lower lines represent the 10%–90% percentiles of fold change in expression levels of the studied miRNAs. Data were analyzed by Mann-Whitney U-test. Groups with ( $P<$ 0.05) show significant difference, while those with ( $P>$ 0.05) show no significant difference. (A) miR-676-3p (B) miR-512-5p (C) miR-650 (D) miR-552-3p (E) miR-147b.

Individual miRNA expression signature in every fibrotic stage (f1 to f4) was compared in order to monitor disease progression. Expression profile for miR-650 exhibited significant upregulation in early fibrotic (f1-f2) group as compared to healthy controls meanwhile miR-147b exhibited significant downregulation ( $P=$ 0.0023 and $P<$ 0.0001 respectively) (Fig. 2). Comparing late fibrotic(f3-f4) group to healthy controls revealed significant upregulation in expression of miR-676-3p, miR-512-5p and miR-552-3p ( $P=$ 0.0338, $P=$ 0.029 and $P=$ 0.0002 respectively) in addition to significant downregulation in miR-147b expression ( $P<$ 0.0001) (Fig. 2). Furthermore, HCC patients showed significant downregulation in expression profiles for miR-676-3p and miR-650 as compared to (f3-f4) group ( $P=$ 0.0029 and $P<$ 0.0001 respectively) (Fig. 2).

Figure 2.

Signature of plasma miRNAs relative expression profiles in healthy, f0, f1-f2, f3-f4and HCC groups. Data were analyzed by Mann-Whitney U-test. Groups with ( $P<$ 0.05) show significant difference, while those with ( $P>$ 0.05) show no significant difference. The box represents the 25%–75% percentiles; the line inside the box represents the median and the upper and lower lines representing the 10%–90% percentiles of fold change in expression levels of the studied miRNAs. (A) miR-676-3p (B) miR-512-5p (C) miR-650 (D) miR-552-3p (E) miR-147b.

Figure 3.

Signature of plasma miRNAs relative expression profiles in healthy, f0, f1-f3, cirrhosis and HCC groups. Data were analyzed by Mann-Whitney U-test. Groups with ( $P<$ 0.05) show significant difference, while those with ( $P>$ 0.05) show no significant difference. The box represents the 25%–75% percentiles; the line inside the box represents the median and the upper and lower lines representing the 10%–90% percentiles of fold change in expression levels of the studied miRNAs. (A) miR-676-3p (B) miR-512-5p (C) miR-650 (D) miR-552-3p (E) miR-147b.

Figure 4.

Signature of plasma miRNAs relative expression profiles in Non HCC and HCC groups. The box represents the 25%–75% percentiles; the line inside the box represents the median and the upper and lower lines represent the 10%–90% percentiles of fold change in expression levels of the studied miRNAs. Data were analyzed by Mann-Whitney U-test. Groups with ( $P<$ 0.05) show significant difference, while those with ( $P>$ 0.05) show no significant difference. (A) miR-676-3p (B) miR-512-5p (C) miR-650 (D) miR-552-3p (E) miR-147b.

Figure 5.

Fold change of miRNA expression levels in non-cirrhotic and cirrhotic group. The box represents the 25%–75% percentiles; the line inside the box represents the median and the upper and lower lines represent the 10%–90% percentiles of fold change in expression levels of the studied miRNAs. Data were analyzed by Mann-Whitney U-test. Groups with ( $P<$ 0.05) show significant difference, while those with ( $P>$ 0.05) show no significant difference. (A) miR-676-3p (B) miR-512-5p (C) miR-650 (D) miR-552-3p (E) miR-147b.

Table 2

Diagnostic performances of the studied plasma miRNAs to discriminate HCC, CLD, cirrhotic patients and healthy controls from each other’s. Significance is indicated by ( $P<$ 0.05) while no significance is indicated by ( $P>$ 0.05)

MiRNAs	AUC	Best cutoff	Sensitivity	Speci city	Std. error	95% confidence interval	$p$ -value
miR-676-3p
Healthy control vs HCC	0.7188	$<$ 0.7420	71.88	100	0.07948	0.5629 to 0.8746	0.002646
Healthy control vs CLD	0.6842	$<$ 0.9605	68.42	100	0.07541	0.5364 to 0.8320	0.005145
Healthy control vs cirrhosis	0.7226	$>$ 1.089	72.22	100	0.07456	0.5764 to 0.8687	0.0008617
CLD vs cirrhosis	0.7083	$>$ 0.9605	75	68.42	0.06184	0.5871 to 0.8296	0.002066
Non cirrhotic vs cirrhotic	0.7155	$>$ 1.089	72.22	84.62	0.06252	0.5929 to 0.8380	0.000228
Cirrhosis vs HCC	0.7405	$<$ 0.8465	71.88	75	0.06122	0.6204 to 0.8605	0.0006706
NON HCC vs HCC	0.6690	$<$ 0.7650	71.88	71.93	0.05932	0.5527 to 0.7853	0.0035
miR-512-5p
Healthy control vs HCC	0.6420	$<$ 0.9810	60.71	100	0.09044	0.4647 to 0.8193	0.04761
Healthy control vs cirrhosis	0.7692	$<$ 0.9180	76.92	100	0.08263	0.6072 to 0.9312	0.0004653
CLD vs cirrhosis	0.6928	$<$ 0.5175	61.54	66.67	0.06510	0.5652 to 0.8204	0.008875
Non cirrhotic vs cirrhotic	0.7315	$<$ 0.5175	61.54	83.54	0.0645	0.6050 to 0.8580	0.0004
miR-650
Healthy control vs HCC	0.8400	$<$ 0.9145	84	100	0.07332	0.6963 to 0.9837	$<$ 0.0001
Healthy control vs CLD	0.6279	$>$ 1.006	62.79	100	0.07371	0.4834 to 0.7724	0.04502
Cirrhosis vs HCC	0.7973	$<$ 0.5480	76	78.38	0.05658	0.6864 to 0.9082	$<$ 0.0001
CLD vs HCC	0.7837	$<$ 0.9255	84	62.79	0.05409	0.6777 to 0.8898	0.0001061
NON HCC vs HCC	0.8067	$<$ 0.6795	80	80.83	0.04745	0.7136 to 0.8997	$<$ 0.0001
miR-552-3p
Healthy control vs HCC	0.8148	$<$ 0.8885	81.48	100	0.07476	0.6683 to 0.9614	$<$ 0.0001
Healthy control vs CLD	0.6410	$<$ 0.9110	64.1	100	0.07681	0.4904 to 0.7916	0.03103
Healthy control vs cirrhosis	0.7419	$<$ 0.9595	74.19	100	0.07859	0.5879 to 0.8960	0.0009751
Non cirrhotic vs cirrhotic	0.6560	$<$ 0.7225	67.74	72.15	0.06555	0.5275 to 0.7845	0.011
Non HCC vs HCC	0.676	$<$ 0.5320	74.07	70.91	0.05737	0.5645 to 0.7894	0.0044
miR-147b
Healthy control vs CLD	0.7647	$<$ 0.9205	76.47	100	0.07275	0.6221 to 0.9073	0.0002221
Healthy control vs cirrhosis	0.8571	$<$ 0.7900	85.71	100	0.05915	0.7412 to 0.9731	$<$ 0.0001
Non cirrhotic vs cirrhotic	0.72	$<$ 0.6455	85.71	70.27	0.05878	0.6120 to 0.8425	0.0001

Comparing mild fibrotic(f0-f1) group to healthy controls similarly revealed significant downregulation in miR-147b expression in (f0-f1) group ( $P=$ 0.0002) in addition to significant upregulation in miR-676-3p ( $P=$ 0.0009) (Supplementary Fig. 1). There was also significant upregulation in miR-676-3p expression in cirrhotic group as compared to (f0-f1) group ( $P=$ 0.0017) (Supplementary Fig. 1). Expression profile for miR-512-5p showed significant decrease in (f4) stage as compared to advanced fibrotic (f2-f3) group ( $P=$ 0.008) (Supplementary Fig. 1). HCC group showed noticeable downregulation in miR-650 expression as compared to both (f0-f1) and (f2-f3) groups ( $P<$ 0.0001) (Supplementary Fig. 1). Moreover, the (f0-f2) combined fibrotic group showed similar expression pattern to the (f0-f1) group especially for miR-147b against healthy controls, miR676-3p against cirrhotic group and also miR-650 against HCC group (Supplementary Fig. 2). In addition to that, f3 group showed significant downregulation in miR-676-3p expression as compared to (f4) group ( $P=$ 0.0108) and also significant upregulaton in miR-650 expression as compared to the HCC group. In further analysis, the profile signatures for all the studied miRNAs were investigated in the combined (f1-f3) fibrotic group against control, (f4) cirrhotic, f0 and also HCC groups. Similar results were observed when comparing (f1-f3) group to healthy controls where only miR-676-3p was upregulated in (f1-f3) group ( $P=$ 0.0075) while miR-147b was downregulated ( $P<$ 0.0001) (Fig. 3). The f0 group showed significant upregulation in miR-512-5p expression as compared to the cirrhotic group ( $P=$ 0.0034) (Fig. 3) meanwhile there was significant downregulation in miR-676-3p as compared to the cirrhotic group ( $P=$ 0.0128) (Fig. 3). The f1-f3 fibrotic group showed significant downregulation in miR-676-3p expression as compared to the cirrhotic group ( $P=$ 0.0111) (Fig. 3). Both f0 and f1-f3 groups exhibited significant upregulation in miR-650 expression profiles when compared to the HCC group ( $P<$ 0.0001) (Fig. 3).

Combining (f0-f4) categories revealed also significant upregulation in the expression profiles of miR-512-5p and miR-552-3p ( $P=$ 0.0425 and $P=$ 0.0026 respectively) as compared to healthy controls while miR-147b remained downregulated in the combined (f0-f4) group ( $P<$ 0.0001) (Supplementary Fig. 3). Moreover, the expression profiles for miR-676-3p and miR-650 were significantly upregulated in the combined (f0-f4) group ( $P=$ 0.0208 and $P<$ 0.0001 respectively) as compared to HCC (Supplementary Fig. 3). Comparing miRNAs expression profiles in HCC group against non-HCC (healthy controls, CLD & cirrhotic) revealed that miR-676-3p ( $P=$ 0.0032), miR-650 ( $P<$ 0.0001) and miR-552-3p ( $P<$ 0.0040) were significantly down regulated in HCC group (Fig. 4). Comparing cirrhotic against non-cirrhotic group revealed that miR-676-3p ( $P=$ 0.0002) and miR-552-3p ( $P=$ 0.0094) were remarkably upregulated in cirrhotic group meanwhile miR-512-5p ( $P=$ 0.0003) and miR-147b ( $P<$ 0.0001) were significantly downregulated in cirrhotic group (Fig. 5).

3.3 Diagnostic performance for the studied plasma miRNAs

ROC analysis revealed that miR-650 was the best among all other studied miRNAs in discriminating HCC from healthy controls with an AUC $=$ 0.840 ( $P<$ 0.0001) with reported specificity and sensitivity values of 100% and 84% respectively (Table 2). This was followed by miR-552-3p (AUC $=$ 0.8148, $P<$ 0.0001) and miR-676-3p (AUC $=$ 0.714, $P=$ 0.002646). The investigated miRNAs could also discriminate between CLD and healthy controls with an AUC $=$ 0.7647 for miR-147b ( $P<$ 0.0001) with 100% specificity and 76.47% sensitivity. To discriminate cirrhotic from healthy controls, miR-147b recorded the highest AUC $=$ 0.857 ( $P<$ 0.0001) with 100% specificity and 85.71% sensitivity. The diagnostic performance for miR-650 in discriminating HCC from CLD groups exhibited an AUC $=$ 0.783 ( $P=$ 0.0001). To discriminate HCC from cirrhotic group, miR-650 and miR-676-3p showed significant performance with AUC $=$ 0.797 ( $P<$ 0.0001) and AUC $=$ 0.7405 ( $P=$ 0.0006) respectively. Furthermore, the best diagnostic performance in discriminating cirrhosis from CLD was obtained for both miR-676-3p and miR-512-5p with AUC $=$ 0.708 ( $P=$ 0.0020) and AUC $=$ 0.692 ( $P=$ 0.008) respectively. The following miRNAs were able to discriminate between HCC and non-HCC; miR-650 (AUC $=$ 0.806, $P<$ 0.0001), miR-552-3p (AUC $=$ 0.676, $P=$ 0.004). For discrimination between cirrhotic and non-cirrhotic groups; miR-147b and miR-676-3p exhibited AUC $=$ 0.72 ( $P=$ 0.0001, 85.7% sensitivity and 70.3% specificity) and AUC $=$ 0.71 ( $P=$ 0.0002, 84.6% specificity and 72% sensitivity) respectively.

3.4 Correlations between the studied plasma miRNAs in various HCV mediated liver diseases

There were various positive and significant correlations between the studied miRNAs in different stages of HCV mediated liver diseases. For instance, miR-676-3p was correlated with marginal significance not only with miR-512-5p (Spearman $r=$ 0.573, $P=$ 0.051) but also with miR-552-3p (Spearman $r=$ 0.544, $P=$ 0.055) in the early fibrotic group. In the combined fibrotic (f1-f3) group, miR-676-3p was correlated with miR-512-5p (Spearman $r=$ 0.463, $P=$ 0.046). In the cirrhotic and in the combined (f0-f4) groups, miR-650 was correlated with miR-512-5p (Spearman $r=$ 0.452, $P=$ 0.018) and (Spearman $r=$ 0.318, $P=$ 0.010) respectively. In the late fibrotic (f3-f4) group, miR-676-3p was slightly correlated with miR-552-3p (Spearman $r=-$ 0.313, $P=$ 0.056). Combining all patients together revealed strong correlation between miR-676-3p and miR-650 (Spearman $r=$ 0.544, $P=$ 0.004). There was no significant correlation between the studied miRNAs in HCC group.

3.5 Logistic regression analysis of miRNAs

Univariate and multivariate logistic regression analyses revealed that miR-650 and miR-552 were significant predictors associated with the chances of HCC diagnosis with an AUC value of 0.6320. The same miRNA panel could also discriminate HCC from healthy controls (AUC $=$ 0.7861), HCC from CLD (AUC $=$ 0.6871), HCC from cirrhosis (AUC $=$ 0.6663). Considering cirrhotic versus non-cirrhotic group, multivariate analyses revealed that miR-676-3p and miR-512-5p were significant predictors of the risk of being diagnosed with cirrhosis with exhibited AUC $=$ 0.6016. The same miRNA panel could also discriminate cirrhotic patients from healthy controls (AUC $=$ 0.6943). Concerning CLD versus cirrhosis, miR-676-3p, miR-650 and miR-512-5p were selected as significant predictors associated with the chances of cirrhotic diagnosis (AUC $=$ 0.5549). Concerning healthy controls versus CLD subgroup, expression levels for miR-650 and miR-512-5P were selected as significant predictors associated with the chances of CLD (f0-f3) diagnosis (AUC $=$ 0.7442).

3.6 Target prediction analysis

Based on the TargetScan and miRDB prediction programs, miR-676-3p, miR-512-5p, miR-650, miR-552-3p and miR-147b were found to strongly target genes related to fibrosis, cirrhosis and HCC development and progression. We closely reviewed the putative targets for the chosen miRNAs in order to explain the significance of the dysregulation of their expression in the context of HCV related hepatic diseases. The putative targets for miR-676 are PTP1B (protein tyrosine phosphatase, receptor type-B); SMURF2 (SMAD specific E3 ubiquitin protein ligase-2); ANP32B (acidic nuclear phosphoprotein 32 family member-B); TAB2 (TGF-beta activated kinase-1 (MAP3K7) binding protein-2); SCN8A (sodium voltage-gated channel alpha subunit-8) and B3GALNT2 (beta-1,3-N-acetylgalactosaminyltransferase-2). The predicted targets for miR-512 are; HLTF (helicase like transcription factor); PTMA (prothymosin alpha); DDX6 (DEAD-box helicase 6) and ATRX (ATRX, chromatin remodeler). The putative targets for miR-650 are; AXL (AXL receptor tyrosine kinase); EBF3 (EBF transcription factor 3); TRAF4 (TNF receptor associated factor 4); Rac1 (Ras-related C3 botulinum toxin substrate 1); NCOA1 (nuclear receptor coactivator 1) and KLF6 (Kruppel like factor 6). The possible targets for miR-552-3p are; TAOK1 (TAO kinase1); NLK (nemo like kinase); SAMD9L (sterile alpha motif domain containing 9 like); DTWD1 (DTW domain containing 1); TSC22D1 (TSC22 domain family member 1). TSPYL5 (TSPY like 5) and NLK (nemo like kinase).The putative targets for miR-147b are NDRG4 (NDRG family member 4) and BDNF (brain derived neurotrophic factor). Target prediction analysis for all investigated miRNAs is summarized in (Table 3).

4. Discussion

Recently circulating miRNAs have attracted great

Table 3
Targets prediction analysis using TargetScan and miRDB prediction programs for the five investigated miRNAs

miRNAs	Targets	Expression in fibrosis and/or cirhosis	Expression in HCC
miR-676-3p	PTPRB (protein tyrosine phosphatase, receptor type B)	Upregulated in fibrosis [59]	Down regulated in HCC [60, 61]
Upregulated in CLD, cirrhosis and HCC	SMURF2 (SMAD specific E3 ubiquitin protein ligase 2)	Upregulated in fibrosis, Upregulated in early stages of fibrosis and downregulated in cirrhosis [62, 63]	Not reported
	ANP32B (acidic nuclear phosphoprotein 32 family member B)	Downregulated in fibrosis [64]	Down regulated in HCC [64]
	TAB2 (TGF-beta activated kinase 1 (MAP3K7) binding protein 2)	Downregulated in fibrosis [65]	Up regulated in HCC [66]
	SCN8A (sodium voltage-gated channel alpha subunit 8)	Not reported	Up regulated in HCC [67]
	B3GALNT2 (beta-1,3-N-acetylgalactosaminyltransferase 2)	Not reported	Up regulated in HCC [68]
miR-512-5p	HLTF (TSG) helicase like transcription factor	Not reported	Down regulated in HCC [1]
Downregulated in cirrhosis	PTMA (prothymosin alpha)	Not reported	Upregulated in HCC [75]
Upregulated in HCC	DDX6 (DEAD-box helicase 6)	Upregulated in HCV-related diseases [76]	Upregulated in HCC [76]
	ATRX (ATRX, chromatin remodeler)	Not reported	Down regulated in HCC [77]
miR-650	Rac1 (Ras-related C3 botulinum toxin substrate 1) (Oncogene)	Upregulated in fibrosis [83]	Upregulated in HCC [84]
Upregulated in CLD	AXL (AXL receptor tyrosine kinase)	Upregulated in fibrosis [85]	Upregulated in HCC [85]
Downregulated in HCC	EBF3 (EBF transcription factor 3)	Not reported	Upregulated in HCC [89]
	NCOA1 (nuclear receptor coactivator 1)	Not reported	UPregulated in HCC [86]
	KLF6 (TSG) (Kruppel like factor 6)	Not reported	Deregulated by loss and/or mutation in HCC [87]
	TRAF4 (TNF receptor associated factor)	Not reported	Upregulated in HCC [88]
miR-552-3p	TAOK1 (TAO kinase 1)	Upregulated in fibrosis [99]	Not reported
Upregulated in CLD and cirrhosis	TSPYL5 (TSPY like 5)	Not reported	Upregulated [95]
Downregulated in HCC	DTWD1 (DTW domain containing 1)	Not reported	Down regulated in gastric cancer [96]
	NLK (nemo like kinase) (oncogenic)	Not reported	Upregulated [97, 98]
	TSC22D1 (TSC22 domain family member 1) (TSG)	Not reported	Downregulated [100]
	SAMD9L (sterile alpha motif domain containing 9 like)	Not reported	Downregulated in HBV related HCC [101]
miR-147b	NDRG4 (NDRG family member 4)	Upregulated in liver fibrosis by HCV [109]	Not reported
Downregulated in CLD and cirrhosis	BDNF (brain derived neurotrophic factor)	Down regulated in non alcoholic fatty liver with fibrosis and Down regulated in cirrhosis [107, 108]	Not reported

attention as stable, promising and reliable biomarkers in several diseases with remarkable changes in their expression levels in response to physiological and pathological conditions [21]. Their expression profiles have been quantified mainly using either RNA-Seq, microarrays or RT-PCR platform [17, 18, 24, 25, 26, 27, 51, 52, 53, 54, 55]. In this study and for the first time, we reported that miR-676 was upregulated in CLD, mild fibrosis (f0-f1), (f1-f3), late fibrosis (f3-f4), cirrhotic and HCC groups. To the best of our knowledge, no study was conducted previously to evaluate the expression profile for miR-676 in liver related disorders. Previous studies reported that it was down-regulated in the tissues of aggressive neuroblastoma tumor phenotype [56], in signet-ring cell carcinomas [38] and in breast cancer [57]. On the contrary, it was upregulated in the plasma of colorectal cancer patients [58]. Based on the TargetScan and miRDB prediction programs, The putative targets for miR-676 were; i) PTP1B which was elucidated for its tumor-suppressing characteristics and is likely found to be upregulated in fibrosis and down regulated in HCC [59, 60, 61]; ii) SMURF2 which was upregulated in fibrosis but down regulated in cirrhosis [62, 63]; iii) ANP32B which was reported to be down regulated in fibrosis and HCC [64]; iv) TAB2 that regulates the activity of TAK1 kinase which is a key modulator in activating NF- $\kappa$ B in response to TNF and other pro-inflammatory cytokines and was found to be upregulated in fibrosis and down regulated in HCC [65, 66]; v) SCN8A and B3GALNT2 which were reported to be up regulated in HCC [67, 68]. Accordingly, we hypothesized that either up-regulation or down regulation of the above mentioned genes was a result of the up-regulation of their regulator miR-676. It seems to play a key role in regulating oncogenic process and would therefore be a potential biomarker candidate for HCC arising from the high-risk HCV positive population and it might also be a useful biomarker for fibrotic and cirrhotic cases as well (Table 3).

Concerning miR-512-5p, it was down regulated in cirrhotic group, CLD, (f0), advanced fibrosis and also in non-cirrhotic group. On the other hand, it was upregulated in HCC, (f0-f4) and late fibrosis. Previous studies did not focus on miR-512-5p expression profile during various stages of HCV mediated liver disorders. For instance, its levels were reported to be upregulated only in cells of HCC [69], colorectal and liver metastasis [70, 71]. This aligns well with our findings for HCC category. On the contrary, it was reported that miR-512-5p was down regulated in 4 different HCC cell lines [72]. Moreover, miR-512-5p was found to be down regulated in other cancer types such as gastric cancer [73], head and neck squamous cell carcinoma (HNSCC) [74]. TargetScan and miRDB prediction programs revealed the following putative targets for miR-512-5p; i) HLTF which plays an important role in carcinogenesis as it was suggested to be a tumor suppressor gene since it was found to be down regulated in HCC [1]; ii) PTMA which was found to be upregulated in HCC [75]; iii) DDX6 was found to be upregulated in HCV related diseases and in HCC [76]; iv) ATRX was down regulated in HCC [77]. Accordingly, we hypothesized that over expression or down expression of the above mentioned genes might be regulated through miR-512 dysregulation which was upregulated in our HCC patients and down regulated in cirrhosis and CLD groups. Therefore, miR-512 seems to be a possible oncogenic regulator and could serve also as a promising biomarker candidate for HCC, cirrhosis and CLD associated with HCV chronic infection (Table 3).

In this study, miR-650 levels were down regulated in HCC as compared to remaining groups. On the other hand, it was up regulated in CLD, (f1-f2) as compared to healthy controls. This comes in agreement with Abdalla & Haj-Ahmad study (2012) which reported down-regulation of miR-650 in urine samples of HCC-post HCV positive group [78]. This suggests that it may serve as a potential marker for early diagnosis of HCC among high-risk HCV patients. Contradictory to this, miR-650 was found to be upregulated in the tissues of different cancer types such as HCC [30, 79], colorectal cancer [80], prostate cancer [81] and gastric cancer [82].

Based on the TargetScan and miRDB prediction programs, the putative targets for miR-650 are; TRAF4, Rac1, AXL, EBF3 and NCOA1 which are most likely to play an important role in carcinogenesis as it was suggested that their over-expression could promote tumorigenesis. On the other side, KLF6 is considered to be a tumor suppressor gene which was found to be deregulated in HCC. Rac1 and AXL were also found to be upregulated in fibrosis. Accordingly, we hypothesized that the over-expression of TRAF4, Rac1, AXL and NCOA1 along with the down regulation of KLF6 could be regulated by miR-650 [83, 84, 85, 86, 87, 88, 89]. Therefore, miR-650 could be a possible tumor-suppressor regulator and could serve as a potential candidate biomarker for HCC and fibrosis (Table 3).

Concerning miR-552-3p, it was found to be upregulated in cirrhosis, CLD, (f3-f4), (f0-f4) and downregulated in HCC. Its expression in the tissues of several types of cancer was reported to increase in HCC [34], colon cancer [90, 91], ovarian and gastric cancer as well [92, 93, 94]. TargetScan and miRDB software predicted the following putative targets for miR-552-3p; DTWD1, TSC22D1, SAMD9L which play an important role in carcinogenesis as they were reported to be down regulated in tumorigenesis. On the contrary, TSPYL5 and NLK are considered to be oncogenes which were found to be upregulated in HCC [95, 96, 97, 98] meanwhile TAOK1 was found to be upregulated in fibrosis [99]. Accordingly, we hypothesized that the over-expression of TSPYL5 and NLK, and the down regulation of DTWD1, TSC22D1 and SAMD9L could be initiated as a result of down-regulating miR-552-3p [95, 96, 97, 98, 99, 100, 101]. We can therefore hypothesize that miR-552-3p could serve as a possible tumor-suppressor regulator and its measurements could assist in early screening for HCV associated HCC and fibrosis (Table 3).

Concerning miR-147b, it was found to be down regulated in cirrhotic, CLD, (f0-f1), (f0-f2), (f1-f3), (f1-f2), (f3-f4) and (f0-f4). Another study reported significant upregulation of miR-147-b in HCV DLBCL (diffuse large B-cell lymphoma) [102]. Its expression in lung and ovarian cancer was also reported to be upregulated [103, 104]. Contradicting results were reported concerning its expression in CRC [105, 106]. Based on the TargetScan and miRDB prediction programs, the putative targets for miR-147b are NDRG4 which was upregulated in fibrosis and BDNF which was down regulated in non-alcoholic fatty liver fibrosis and was also found to be down regulated in cirrhosis [107, 108, 109]. Accordingly, we hypothesized that the over-expression of NDRG4 and the down regulation of BDNF may result from down-regulation of their regulator miR-147b. MiR-147b could therefore be used as an interesting biomarker for detection of fibrosis and cirrhosis (Table 3).

5. Conclusion

Our study profiled, for the first time, expression signatures for (miR-676, 512-5p, 650, 552-3p and 147b) in the plasma of Egyptian patients affected with various stages of HCV mediated hepatic disorders. The investigated miRNAs seem to be promising in staging and diagnosis of various fibrotic, cirrhotic and HCC complications among Egyptian patients. They could be utilized as potential non-invasive and reliable biomarkers in HCV mediated hepatic disorders. Further clinical evaluation on larger populations with different HCV genotypes would be beneficial.

Author contribution

AAG, MFI and NIS conceived and designed the experiments. SME, NZ and AY provided part of the samples. HS and AAG performed experimental work and statistical analysis. AAG designed primers of this study. AAG and HS drafted the manuscript. MFI, NIS, HS and AAG critically revised the manuscript.

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

Data availability

This article and its supplementary file include all data generated and analyzed during the study.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-210456.

sj-docx-1-cbm-10.3233_CBM-210456.docx - Supplemental material

Supplemental material, sj-docx-1-cbm-10.3233_CBM-210456.docx

Footnotes

Acknowledgments

Authors thank all participating individuals.

Conflict of interest

All authors declare that they have no conflict of interest.

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Diagnosis and staging of HCV associated fibrosis,cirrhosis and hepatocellular carcinoma with target identification for miR-650,552-3p,676-3p,512-5p and 147b

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Subjects

2.2 Routine laboratory tests, RNA extraction and cDNA synthesis

2.3 Quantitative Real-time PCR (qRT-PCR)

Table 1 Primers sequences used for amplification in qRT-PCR

3. Results

3.1 Demographic and clinical features of healthy control, CLD (F0-F3), cirrhotic and HCC patients

3.2 Differential expression of the investigated miRNAs among the studied groups

3.4 Correlations between the studied plasma miRNAs in various HCV mediated liver diseases

3.5 Logistic regression analysis of miRNAs

3.6 Target prediction analysis

4. Discussion

Table 3 Targets prediction analysis using TargetScan and miRDB prediction programs for the five investigated miRNAs

Author contribution

Informed consent statement

Data availability

Supplementary data

sj-docx-1-cbm-10.3233_CBM-210456.docx - Supplemental material

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Primers sequences used for amplification in qRT-PCR

Table 3
Targets prediction analysis using TargetScan and miRDB prediction programs for the five investigated miRNAs