ORIGINAL ARTICLE
miRNA Signature of Hepatocellular Carcinoma Vascularization:How the Controls Can Influence the Signature
Silvia Fittipaldi1 • Francesco Vasuri1 • Sonia Bonora1 • Alessio Degiovanni1 •
Giacomo Santandrea1 • Alessandro Cucchetti2 • Laura Gramantieri3 •
Luigi Bolondi3 • Antonia D’Errico1
Received: 29 March 2017 / Accepted: 13 June 2017 / Published online: 21 June 2017
� Springer Science+Business Media, LLC 2017
Abstract
Background miRNA deregulation and vascular modifica-
tions constitute promising predictors in the study of hepa-
tocellular carcinoma (HCC). In the literature, the relative
miRNA abundance in HCC is usually determined using as
control non-matched tumoral tissue, healthy liver, or cir-
rhotic liver. However, a common standard RNA control for
the normalization toward the tissue gene expression was
not settled yet.
Aim To assess the differences existing in the quantitative
miRNA gene expression in HCC on tissue according to two
different liver controls.
Methods A wide array of miRNAs was analyzed on 22
HCCs arisen in cirrhotic and non-cirrhotic livers by means
of microfluidic cards. Control samples included total RNA
extracted from healthy and cirrhotic livers.
Immunohistochemistry for CD34 and Nestin was per-
formed to assess the pattern of intratumoral vascular
modifications.
Results Six miRNAs were deregulated in HCCs using
either controls: miR-532, miR-34a, miR-93, miR-149#,
miR-7f-2#, and miR-30a-5p. Notably, the miRNA expres-
sion changed significantly between HCCs arisen in cir-
rhotic and non-cirrhotic livers, according to the control
used for normalization. Different miRNA profiles were
found also in HCCs with different vascular patterns,
according to the control used for normalization.
Conclusions Our data confirm that the choice of the
methodology, and particularly the control used for nor-
malization, represents the main concern in miRNA evalu-
ation, particularly in a heterogeneous model such as liver
pathology. Still we observed the deregulation of some
common miRNAs as promising in HCC cancerogenesis
and progression. A standardized control will be a crucial
achievement to compare miRNA expression among dif-
ferent laboratories.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s10620-017-4654-3) contains supplementarymaterial, which is available to authorized users.
& Francesco Vasuri
Silvia Fittipaldi
Sonia Bonora
Alessio Degiovanni
Giacomo Santandrea
Alessandro Cucchetti
Laura Gramantieri
Luigi Bolondi
Antonia D’Errico
1 ‘‘F. Addarii’’ Institute of Oncology and Transplant Pathology,
Department of Specialty, Diagnostic and Experimental
Medicine (DIMES), S. Orsola-Malpighi University Hospital,
V.le Ercolani 4/2, 40138 Bologna, Italy
2 Department of Medical and Surgical Sciences (DIMEC),
S. Orsola-Malpighi University Hospital, Bologna, Italy
3 Division of Internal Medicine, Department of Medical and
Surgical Sciences, University of Bologna, S. Orsola-Malpighi
University Hospital, Bologna, Italy
123
Dig Dis Sci (2017) 62:2397–2407
DOI 10.1007/s10620-017-4654-3
Keywords Cirrhosis � Hepatocellular carcinoma �Microfluidic card � miRNA � Neoangiogenesis
Introduction
Liver resection represents the therapeutic choice for hep-
atocellular carcinomas (HCCs) without concomitant liver
failure. Although post-resection outcome shows substantial
differences, best results are seen in Child-Pugh class A
patients with small lesions without microvascular invasion
(MVI) or metastases and with free surgical margins [1].
The predictors of HCC recurrence are mainly based on
clinical or morphological features, and new research lines
are strongly needed in order to find and validate new
prognostic markers. The study of miRNAs (small RNAs
characterized by regulatory functions on gene expression
that act as gene promoters or suppressors) represents a
promising approach toward further understanding of
human carcinogenesis and tumor progression [2]. Another
important approach is the analysis of neoangiogenesis and
vascular modifications in HCCs. Villa et al. [3] found five
genes involved in neoangiogenesis and/or endothelial
activation to be up-regulated in fast-growing HCCs. Our
group performed immunohistochemical and gene expres-
sion analysis on HCC vascular modifications. We found
four different vascular patterns in HCCs, each of them
characterized by different tumor architecture, vascular
morphology and phenotype, as well as by different
expression of Nestin, IGF1R and TGF-b1 [4].
In HCCs, the biosynthesis pathways of different miR-
NAs were found to promote neoangiogenesis, tumor
invasion and metastasization at different levels, from
transcriptional (e.g., down-regulation of miR-122) [5] and
post-transcriptional regulations (e.g., miR-99 and miR-21)
[6, 7], up to epigenetic and genetic changes [8].
In miRNA studies, choice of methodology is of utmost
importance. Several studies focused on the normalization
of gene expression toward reference genes [9–11]. How-
ever, methodological studies using a standard for normal-
ization toward tissue gene expression are needed. Hitherto,
research has focused on the importance of the reference
genes but not on the appropriateness of the control tissues
[9]. Despite the high number of papers about miRNA,
shared criteria for standardizing the methodology for the
quantification have not been established yet. There is also
lack of details on the sample source, the RNA isolation,
and the control tissues treatment and origin [12]. In the last
decade, some miRNA studies have been carried on with
arrays on human HCC and non-neoplastic tissue, and most
of them utilized adjacent liver tissue for the normalization
[6, 13, 14]. With this approach, Budhu et al. [14] found 20
miRNAs significantly associated with metastases, but only
one miRNA overlapped with the results obtained by
Murakami et al. [13]. In a more recent paper by Sato et al.,
healthy liver tissue was used for the normalization in the
analysis of both HCC and non-neoplastic tissue. Authors
found that different miRNAs were deregulated in both
HCCs and non-neoplastic livers and significantly correlated
with the tumor recurrence after transplantation [15].
Another methodology used the matched non-neoplastic
tissue as control for each HCC, this method is useful to
minimize the individual genetic variability, but it is
expensive and prevents the identification of a universal
control RNA with intralaboratory reproducibility. [16–18].
Thus, according to the goals of the studies, different con-
trols were selected to assess differential miRNA
expression.
The choice of the control tissues as reference for nor-
malization in each analysis is the key point, since it was
demonstrated that results can vary widely with different
reference controls, and a single miRNA can turn out to be
down-regulated in an experiment and up-regulated in
another, just by changing the control [19, 20]. The topic is
even more tangled in the study of liver tumors, where the
control tissue is seldom represented by ‘‘healthy’’ liver,
since most HCCs arise in a cirrhosis or in a chronic hep-
atitis. In the perspective of a miRNA-regulatory therapy,
this could be a challenge [20].
We hereby will demonstrate the concern and the dif-
ferences that emerged by using two different control tissues
to normalize miRNA gene expression in HCC. These two
controls represent the opposite biological backgrounds in
HCCs pathogenesis; total RNA was extracted from virus-
free and cancer-free healthy livers, as well as from HCV-
related cirrhotic livers. The different miRNA profiles of
resectable HCCs arisen in cirrhotic (c-HCC) and non-cir-
rhotic (nc-HCC) livers will be determined. Finally, miRNA
expression will be correlated with the patterns of vascular
modifications of HCCs, in order to confirm the existence of
different steps in HCC progression. The identification of
pattern-specific miRNAs possibly correlated with neoan-
giogenesis and tumor architecture could contribute to better
understand HCCs behavior.
Methods
Ethics
The present tissue study was approved in advance by the
Ethical Committee of the S. Orsola-Malpighi University
Hospital (protocol code APHCC-2012, reference number
85/2013/O/Tess). All patients were treated according to the
2398 Dig Dis Sci (2017) 62:2397–2407
123
ethical guidelines of the 1975 Declaration of Helsinki (6th
revision, 2008); informed consent was obtained from each
patient at the time of surgery.
Case Selection, Histopathology
and Immunohistochemistry
We retrospectively selected 22 consecutive patients sub-
mitted to surgical resection for HCC. Patients were 19
(86.4%) males and 3 (13.6%) females, mean age at surgery
62.9 ± 9.9 years (range 48–80 years). The etiology of
cirrhosis or chronic liver disease was HCV in 12 cases,
HBV in 5, metabolic in 2, alcoholic in 1, and cryptogenic
in 2.
Tissue was promptly fixed in formaldehyde solution 4%
directly in surgery room and buffered pH 6.9 before routine
processing. For each tumor, we evaluated the occurrence of
cirrhosis, the mean tumor size, Edmondson’s grade, the
occurrence of infiltrative margins, and MVI.
Immunohistochemistry (IHC) for CD34 (mouse mono-
clonal Q-BEnd-10, Dako A/S, pre-Diluted Copenhagen
Denmark) was automatically performed on FFPE 2-lm-
thick sections with Benchmark ULTRA� immunostainer
(Ventana/Roche, Ventana Medical Systems). Nestin
(mouse monoclonal 10C2, 1:400 dilution, Millipore USA)
immunostaining was manually performed as previously
described [21] with NovoLink Polymer Detection Kit
(Novocastra, Newcastle, UK).
The four main patterns of HCC vascular modifications
were determined according to the qualitative sinusoidal and
arteriolar positivity for CD34 and Nestin, as well as tumor
architecture, as previously described (4). Briefly, ‘‘Pattern
a’’ was defined in HCCs with microtrabecular and acinar
architecture, with CD34?/Nestin- sinusoids; ‘‘pattern b’’
in HCCs with similar architecture, but showing CD34?/
Nestin? sinusoids; ‘‘pattern c’’ was defined in HCCs with
macrotrabeculae surrounded by a CD34?/Nestin?
endothelium; ‘‘pattern d’’ characterized solid HCCs with
new-formed CD34?/Nestin? arteries (Fig. 1) [5]. These
vascular patterns were found only in HCCs, as they reflect
neoplastic modifications of the vascularization, and they
are not already occurred in healthy liver and/or in cirrhosis.
To address the important issue of the choice of tissue
control for normalization in miRNA array, we selected two
different pools:
1. A pool composed of 10 healthy livers obtained from
healthy multiorgan donors (five males and five
females; mean age 64.2 ± 10.2 years, range 48–75).
This tissue was retrieved during the histological graft
evaluation for donor safety in our Institution. Of note,
this selected tissue is virus-free and cancer-free.
2. A pool composed of 10 cirrhotic livers obtained from
practice routine analyses (five males and five females;
mean age 55.2 ± 8.4 years, range 46–72), all affected
by HCV-driven end-stage liver disease.
miRNA Arrays
RNA Extraction
A commercial kit was used for RNA extraction from FFPE
sections (FFPE Recover All, Life Technologies, Carlsbad,
CA, USA). Tissue slides were dewaxed with xylene. Tissue
was recovered from the slides with a scalpel by adding
10 ll of digestion buffer. The protocol was followed as
stated by manufacturer’s instruction. The RNA quality and
concentration were evaluated by using a ND-1000 spec-
trophotometer (NanoDrop, Fisher Thermo, USA); RNA
was considered pure if A260/A280 ratio = 1.9–2.1.
Megaplex Reverse Transcription Reaction
The reverse transcription assay was performed using the
TaqMan� MicroRNA Reverse Transcription Kit associated
with the Megaplex RT Primers Pool A and B (Life Tech-
nologies). Samples were loaded in the GeneAmp PCR
System 9700 (Life Technologies) following the manufac-
turer’s thermal conditions.
cDNAs Megaplex Pre-amplification mix
To increase the yield of cDNAs, a second step of pre-
amplification was performed with the TaqMan� PreAmp
Master Mix associated with Megaplex PreAmp Primers
Pool A and B (Life Technologies). Samples were loaded in
the GeneAmp PCR System 9700 following the manufac-
turer’s thermal conditions. Finally, the pre-Amp products
were diluted in 75 lL of H2O.
RT-PCR Arrays
Relative quantitation of targets was performed with Taq-
Man Human microRNA Arrays 384-wells (Gene expres-
sion Micro Fluidic card, Array A v2.1 e Array B v3.0, Life
Technologies). PCR Reaction mix was composed by
450 lL of TaqMan Universal Master Mix II, no Amperase
UNG (Life Technologies), 9 lL of diluted Pre-Amp pro-
duct with primer Pool A or B. (prepare two separate
reaction tubes) and 441 nuclease free water. Dispense
100 lL of the sample-specific PCR reaction mix (Pool A or
B) in each fill port of the 384 plate in Array A or B.
Amplification was performed on the Real Time 7900HT
System (Life Technologies), and data were collected with
Dig Dis Sci (2017) 62:2397–2407 2399
123
the SDS software v2.2 (Life Technologies). Cycling con-
ditions were as follows: 2 min at 50 �C, 10 min at 94.5 �C,30 s at 97 �C (40 cycles), 1 min at 59.7 �C. The tran-
scription levels were normalized using RnU44 as a refer-
ence gene. The expression values for HCC are presented as
fold expression in relation to controls livers; the actual
values were calculated using the 2�DDCT equation, where
DDCT = [CT Target - CT Rnu44](target sample) - [CT
Target - CT GUSB] (control sample).
Statistics and Data Analysis
Variables were expressed as means ± standard deviations,
ranges, and frequencies. MicroRNA Arrays expression was
analyzed with ExpressionSuite Software v1.0 (Life Tech-
nologies). The hypothesis-testing procedure was applied
once the Ct values were normalized with the endogenous
gene and the control groups. Student’s t test was applied to
compare the means of the normalized Ct values between
two groups. Resulting P values were adjusted to control the
type I error rate using the Benjamini–Hochberg method
[22]. miRNAs with a significant differential expression
compared to controls were plotted on a Volcano plot.
Results
Histopathology and Vascular Patterns
Mean tumor size of the 22 HCCs analyzed was cm
6.33 ± 4.74 (range cm 1.60–18.0); Edmondson’s grade
was 2 in 1 (4.5%), 3 in 17 (77.3%) and 4 in 4 (18.2%)
cases. HCCs showed infiltrative margins in 19 (86.4%),
and MVI was observed in 17 (77.3%) cases. Twelve
Fig. 1 Architectural appearance and immunohistochemical positivity
(qualitative assessment) for CD34 (left inset) and Nestin (right inset)
of the four patterns of vascular modifications in HCCs. a HCCs with
pattern ‘‘a’’ and b pattern ‘‘b’’ were generally trabecular and acinar,
with CD34?/Nestin- sinusoids in ‘‘a’’ and CD34?/Nestin?
sinusoids in ‘‘b’’; c pattern ‘‘c’’ HCCs showed macrotrabeculae
surrounded by a CD34?/Nestin? endothelium; (D) HCCs with
pattern ‘‘d’’ showed a vasculature principally made by newly formed
CD34?/Nestin? arteries. Hematoxylin–Eosin stain, magnification
910 and 920 (insets); scale bar 100 lm
2400 Dig Dis Sci (2017) 62:2397–2407
123
Fig. 2 Significant differential miRNA expression in HCC in relation
to the control group, healthy liver (red bars) or cirrhotic liver (blue
bars). The X axis represents the log10 (fold change); down-regulated
miRNAs are on the left part of the Y axis and up-regulated miRNAs
are on the right part of the Y axis
Dig Dis Sci (2017) 62:2397–2407 2401
123
(54.5%) HCCs arose in a cirrhotic liver (c-HCC) and 10
(45.5%) in a non-cirrhotic livers (nc-HCC).
According to HCC architecture and IHC for CD34 and
Nestin, the HCCs of our series were sorted in vascular
‘‘pattern a’’ in 2 (9.1%), ‘‘pattern b’’ in 6 (27.3%), ‘‘pattern
c’’ in 8 (36.3%), and ‘‘pattern d’’ in 6 (27.3%) cases
(Fig. 1) (4).
miRNA Analysis I: Deregulated miRNAs in HCCs
The first step of our study was to assess the miRNAs
deregulated in HCC. After the Benjamini–Hochberg
posttest, we found 14 miRNAs significantly deregulated
compared to the healthy liver control pool (see Figs. 2, 3,
Supplemental table S1). In particular, 5 miRNAs were
down-regulated (mean fold increase 0.12 ± 0.07) and 9
up-regulated (mean fold increase 8.77 ± 7.42). Using the
cirrhotic liver control pool, we noticed that 53 miRNAs
were significantly deregulated. In particular, 44 were
down-regulated (mean fold increase 0.10 ± 0.11), and 9
up-regulated (mean fold increase 103.55 ± 223.29).
Fifty-five miRNAs gave results depending on the tissue
used for normalization: 8 miRNAs were limited to the
analysis using the healthy liver control pool, while 47 were
limited to the analysis using the cirrhotic liver control pool.
Conversely, the deregulation of 6 miRNAs was common
between the two analyses, 5 with the same trend (miR-34a,
miR-93, miR532, miR-149#, and miR-7f-2#) and 1 with the
opposite trend (miR-30a-5p) (Fig. 3). Among these 6 sig-
nificant miRNAs, 5 are already known to be associated with
HCC or with other human cancers (Table 1). No correlations
emerged with etiology in our series (data not shown), except
for miR-149# that showed a significant down-regulation in
HCC with HCV- and HBV-driven chronic liver disease
(P\ 0.001). All raw data prior to statistical analysis are
found in the Supplemental table S2 (sheet 1).
miRNA Analysis II: Deregulated miRNAs in c-
HCCs and nc-HCCs
When the healthy liver control pool is used for normal-
ization, 14 miRNAs were significantly down-regulated in
Fig. 3 Volcano plots representing miRNAs deregulated in HCC
according to the normalization. a compared to the non-cirrhotic and
b cirrhotic liver. The X axis represents the log2 (fold change) and the
Y axis the -log10 (P value). miRNAs with statistically significant
differential expression (P\ 0.05 adjusted for multiple testing and
fold change boundary at 2) are located above the horizontal threshold
blue line and outside the pair of vertical threshold black lines. Down-
regulated miRNAs are found in the upper left (green) of the plot and
up-regulated miRNAs in the upper right parts (red). The 6 miRNAs in
common are miR-34a, miR-93, miR532, miR-149#, miR-7f-2# and
miR-30a-5p
2402 Dig Dis Sci (2017) 62:2397–2407
123
HCC tissue (Table 2, Supplemental table S2, sheet 2):
particularly, only miR-1178 was significantly down-regu-
lated in c-HCCs, while the remaining 13 miRNAs were
deregulated in nc-HCCs. Among these 14 miRNAs, 5 were
in common with those resulting in analysis I (all 22 HCCs):
miR-149#, miR-23a#, miR-640, miR-1259, and miR-7f-2#.
Remarkable results were obtained using the cirrhotic
liver control pool for normalization: 120 miRNAs were
found to be deregulated (Table 2, Supplemental table S2,
sheet 2), 37 in c-HCCs (all down-regulated but 2) and 83 in
nc-HCCs (all down-regulated). Forty-three out of 120
miRNAs were common with those observed in general
HCCs (analysis I). Furthermore, 29 down-regulated miR-
NAs were common to c-HCCs, nc-HCCs and the overall
HCCs, in all cases with the same trend, confirming the
results of analysis I. It is crucial to notice that the pool of
cirrhotic control tissues might not be an appropriate model
for the normalization of nc-HCCs.
Three miRNAs (miR-136#, miR-149# and 188-3p) were
significantly down-regulated independently from the ana-
lyzed tissues (c-HCC or nc-HCC) and the pool used to
compare the fold change (Supplemental table S2, sheet 2).
miRNA Analysis III: Deregulated miRNAs in HCCs
with Different Vascular Patterns
The analysis of the miRNAs expressed in HCCs with dif-
ferent vascular modifications showed that each of the four
vascular patterns is characterized by the differential
expression of miRNAs. In particular, compared to the
healthy liver control pool, 16 miRNAs were significantly
deregulated, 1 in ‘‘pattern a’’ HCCs, 4 in ‘‘pattern b,’’ 6 in
‘‘pattern c’’ and 5 in ‘‘pattern d’’ HCCs (Table 3, Supple-
mental table S2, sheet 3). Compared to the cirrhotic liver
control pool, 112 miRNAs were significantly deregulated,
4 in ‘‘pattern a’’ HCCs (not the same as above), 16 in
‘‘pattern b,’’ 56 in ‘‘pattern c’’ and 36 in ‘‘pattern d’’ HCCs
(Table 3, Supplemental table S2, sheet 3).
Interestingly, HCCs with patterns ‘‘a’’ and ‘‘b’’ share no
miRNAs in common using the two different normaliza-
tions. Conversely, both HCCs with ‘‘pattern c’’ and HCCs
with ‘‘pattern d’’ show 3 common miRNAs, all of them
with similar fold changes, using healthy or cirrhotic liver
control pools as reference. ‘‘Pattern a’’ HCCs are the only
biological group showing up-regulation of 2 miRNAs
(miR-551b# and miR-1825), while all other cases were
down-regulated (all data are found in the Supplemental
table S2, sheet 3). Patterns ‘‘a’’ and ‘‘b’’ HCCs do not
conserve the same profile using different control tissues,
while pattern ‘‘c’’ and ‘‘d’’ seem to show peculiar miRNA
profiles (see ‘‘Discussion’’).
Discussion
The present study focused on a series of surgically resected
histologically advanced HCCs, with the aim (1) to define
the different miRNA profiles of HCCs arisen in cirrhotic
and non-cirrhotic livers based on two different human liver
controls extracted from healthy and cirrhotic livers; (2) to
Table 1 miRNA significantly deregulated in HCC independently on the control used, and references to the literature
miRNA expression Known interaction with HCC Reference(s)
miR-34a (UP) HCC progression and aggressiveness
(p53 and b-catenin pathways)
Recurrence prediction after radiofrequency ablation in early HCC
Clinical trial miR-RX34 in patients with primary liver cancer
Budhu et al. [14]
Huang et al. [16]
Pineau et al. [30]
Dang et al. [25]
Gougelet et al. [26]
Cui et al. [27]
miR-93 (UP) HCC progression
Drug sensitivity in vitro
Huang et al. [16]
Pineau et al. [30]
Xu et al. [28]
Shi et al. [29]
miR-532 (UP) Unknown …miR-149# (DOWN) HCC tumorigenesis (AKT/mTOR pathway) Zhang et al. [7]
let-7f-2# (DOWN) Let-7f dysregulated in HCC Gramantieri et al. [31]
Huang et al. [16]
Shimizu et al. [33]
miR-30a-5p (UP/DOWN) HCC progression Wojcicka et al. [34]
Dai et al. [35]
Dig Dis Sci (2017) 62:2397–2407 2403
123
correlate miRNA expression with the patterns of tumor
vascular modifications according to the same two controls.
The tissue used for normalization represents a key point in
order to identify miRNAs deregulation in HCCs. Since
HCCs can arise in both cirrhotic and non-cirrhotic livers
(albeit more frequently in the former), we carried out all
analyses using RNA extracted from both tissues, in the
present study. Total RNA extracted from cirrhotic liver
cannot be considered an appropriate control for all cases, in
particular for nc-HCCs. Even if nc-HCCs usually arise in a
context of chronic hepatitis, we decided here to utilize
healthy livers, obtained from healthy multiorgan donors
without virus exposure, chronic liver disease or neoplasia.
Most recent papers on miRNAs have studied both HCC
and cirrhosis using healthy liver tissue as Refs. [6, 13, 14],
with weak overlaps on the identified HCC-related miRNAs,
and often discordance between the up- and down-regulation
of specific miRNAs. This last issue is very important, since
it was postulated that the up- or down-regulation of miR-
NAs could determine the prognosis in HCC patients [17]
and could be used in specific inhibition or restoration
therapies [23]. Beside the factors that can influence the final
results of miRNA analysis, such as the choice of the array
used, the choice of the housekeeping genes, and the statis-
tical approach [24], the selection of the reference control is
a key step that can impact the expression and the fold
Table 2 miRNA deregulated in HCCs arisen in non-cirrhotic (nc-HCC) or cirrhotic (c-HCC) background, compared to the normal or cirrhotic
controls
Control pool Common miRNAs
according to
normalization poolsNormal Cirrhotic
Biological groups
nc-HCC miR-149#, miR-188-3p, miR159a, miR-543,
miR-483-3p, miR-640, miR-1259, miR-
23a#, 7f-2#, miR-1243, miR-136#, miR-
562
miR-30a-3p, miR-1274B, miR-769-3p, miR-617,
miR-720, miR-213, miR-30a-5p, miR-26a-2#,
miR-1260, miR-132#, miR-29b-2#, miR-543,
miR-429, miR-331-5p, miR-635, miR-200c,
miR-542-5p, miR-186#, miR-136, miR-20a#,
miR-571, miR-181c#, miR-24-2#, miR-223#,
miR-376a, miR-99a#, miR-125b-2#, 7f-2#,
miR-144#, miR-590-3P, miR-596, miR-886-5p,
miR-26a-1#, miR-145#, 7a#, miR-650, miR-
380-5p, miR-149#, miR-31#, miR-338-3p, miR-
199b, miR-199a, miR-1208, miR-1288, miR-
214#, miR-126#, miR-483-3p, miR-483-5p,
miR-200a, miR-662, miR-497, miR-1290, miR-
200b, miR-141, miR-644, miR-1247, miR-
130a#, miR-136#, miR-200a#, miR-584, miR-
195#, miR-656, miR-486-3p, miR-604, miR-
646, miR-1255A, miR-380-3p, miR-519a, miR-
609, miR-889, miR-378, miR-639, miR-1305,
miR-188-3p, miR-202#, miR-30c-1#, miR-
377#, miR-452#, miR-520b, miR-520D-3P,
miR-551a, miR-621, miR-875-5p
miR-7f-2#, miR-483-3p,
miR-136#, miR-149#,
miR-188-3p, miR-543
c-HCC miR-1178 miR-130b#, miR-1255B, miR-125b-1#, miR-
99a#, miR-591, miR-125b-2#, miR-20a#, miR-
31#, miR-145#, miR-380-5p, miR-944, miR-
132#, miR-149#-002164, miR-338-3p, miR-
590-3P, miR-101#, miR-26a-1#, miR-126#,
miR-136#, miR-214#, miR-665-002681, miR-
609, miR-200a#, miR-656, 7a#, miR-338-5P,
miR-92b#, miR-378, miR-188-3p, miR-520D-
3P, miR-875-5p, miR-621, miR-551a, miR-
452#, miR-30c-1#, miR-202#, miR-1305
None
Common
miRNAs
between nc-
HCC and
c-HCC
None miR-136#, miR-149#, miR-188-3p, miR-7a#,
miR-125b-2#, miR-126#, miR-1305, miR-132#,
miR-145#, miR-200a miR-200a# miR-202#
miR-20a# miR-214# miR-26a-1# miR-30c-1#,
miR-31#, miR-338-3p, miR-378, miR-380-5p,
miR-452#, miR-520D-3P, miR-551a, miR-590-
3P, miR-656
miR-136#, miR-149#,
miR-188-3p,
2404 Dig Dis Sci (2017) 62:2397–2407
123
increase in miRNAs. The same concept was already
expressed by Visani et al. [19] in brain tumors.
According to our data, when we used the cirrhotic liver
control pool as reference, a greater number of miRNAs was
significantly deregulated in each analysis among biological
groups. Moreover, even the expression of an endogenous
housekeeping gene (U6 snRNA) resulted heterogeneous in
one analysis (Table 3): it could be hypothesized that cir-
rhosis is a too heterogeneous model to be used as control,
albeit representing the background of most HCCs, due to a
high percentage—and variability—of inflammatory cells,
ductular reaction, fibrosis, etc. [17]. For the same reason,
we found many differences in miRNAs deregulation
between c-HCC and nc-HCC (Table 2).
At any chance, our results showed 6 miRNAs to be
deregulated in HCCs, despite the control used: miR-532,
miR-34a, miR-93, miR-149#, miR-7f-2#, and miR-30a-5p.
Apart from miR-532, all of these miRNAs have been
already reported in the HCC literature (Table 1).
miR-34a plays a tumor suppressor role through the
regulation of c-Met and b-catenin pathways [25, 26]. miR-
34a deregulation was linked to HCC progression and
aggressiveness both in vivo and in vitro. Interestingly, a
very recent paper correlated the deregulation of miR-34a
with the recurrence rates of early HCC after radiofrequency
[27].
A deregulation of miR-93 significantly correlated with
poor prognosis in HCCs. In vitro miR-93 activity was also
correlated with HCC sensitivity to sorafenib and tivantinib
[18]: the authors concluded that miR-93 was involved in
cell proliferation, migration, and invasion through the
oncogenic c-Met/PI3 K/Akt pathway, and it also inhibited
apoptosis by PTEN and CDKN1A pathways [18]. Thus, the
use of synthetic regulators of miR-93 may prove to be a
promising approach to liver cancer treatment [28]. Other
studies have found a deregulation of miR-93, together with
other miRNAs, during HCC progression [29, 30].
miR-149 modulates the Akt/mTOR pathway in HCC. A
tumor suppressive role for miR-149, and a prognostic role
in human HCCs were hypothesized [7].
miRNA let-7f-2 was found to be down-regulated in
HCCs [31, 32]. It was seen that the let-7f family of miR-
NAs potentiates sorafenib-induced apoptosis in human
HCC [16]. Recently, a study correlated also serum let-7f
levels with HCC size and early recurrence [33].
Finally, miR-30a-5p expression was altered in HCC
compared to adjacent tissue [34]. In another study, the
overexpression of miR-30a-5p inhibited cell proliferation,
induced apoptosis, increased the number of cells in S
phase, and markedly inhibited invasion and migration of
HCC cells in vitro and in vivo [35].
Table 3 miRNA deregulated in each pattern of vascular modification in HCC compared to the normal or cirrhotic controls
Control pool Common miRNAs
according to
normalization poolsNormal Cirrhotic
Biological groups
Pattern
a
miR-551b# miR-922, miR-548d-5p, miR-516-3p, miR-1825 None
Pattern
b
miR-149#*, miR-1178*, miR-
1243, miR-23a#
U6 snRNA, miR-889, miR-770-5p, miR-662, miR-616, miR-520b,
miR-519b-3p, miR-33a, miR-335#, miR-31#, miR-29a#, miR-
200a#, miR-19b-1#, miR-195#, miR-125b-2#, miR-101#
None
Pattern
c
miR-149#*, miR-1178*, let-
7a#, miR-1300, miR-483-3p,
let-7 g#
miR-29c#, miR-99a#, miR-944, miR-943, miR-92b#, miR-758, miR-
720, miR-708, miR-668, miR-665, miR-661, miR-644, miR-628-
3p, miR-609, miR-604, miR-601, miR-596, miR-592, miR-590-3P,
miR-572, miR-571, miR-497, miR-483-3p, miR-429, miR-33a#,
miR-338-3p, miR-30e-3p, miR-30a-5p, miR-30a-3p, miR-29b-2#,
miR-26a-1#, miR-218, miR-214#, miR-20a#, miR-202#, miR-200c,
miR-188-3p, miR-186#, miR-16-1#, miR-149#, miR-145#, miR-
144#, miR-141, miR-132#, miR-1303, miR-1290, miR-1288, miR-
1267, miR-1260, miR-126#, miR-125b-1#, miR-1228#, miR-1208,
miR-1201, let-7i#, let-7a#
let-7a#, miR-483-3p,
miR-149#
Pattern
d
miR159a, miR-541#, miR-875-
5p, miR-149#*, miR-675
miR-491, miR-875-5p, miR-769-3p, miR-675, miR-656, miR-650,
miR-646, miR-639, miR-621, miR-584, miR-567, miR-551a, miR-
548L, miR-548b, miR-541#, miR-520D-3P, miR-512-5p, miR-
452#, miR-378, miR-377#, miR-30c-1#-, miR-223#, miR-200b,
miR-200a, miR-199b, miR-198, miR-143#, miR-140-3p, miR-136#,
miR-130a#, miR-1305, miR-1298, miR-1274A, miR-1255A, miR-
1247, miR-1225-3P
miR-541#, miR-675,
miR-875-5p
Dig Dis Sci (2017) 62:2397–2407 2405
123
In the study of HCC progression, we sorted HCCs on the
base of different vascular modifications, according to our
recent observations [4]. It is noteworthy, since neoangiogen-
esis plays a pivotal role both in HCC aggressiveness and
metastatic diffusion, and it is responsible from specific radio-
logical features. The present study seems to confirm the pro-
gression from ‘‘pattern a’’ HCCs (characterized by a
microtrabecular architecture,withCD34?/Nestin- sinusoids,
more common in cirrhosis) and the other patterns. Indeed,
‘‘pattern a’’ HCCs showed the deregulation of different miR-
NAs compared to the ‘‘histologically-advanced’’ patterns ‘‘b,’’
‘‘c’’ and ‘‘d’’ HCCs [4]: miR-551b# and miR-1825 were up-
regulated in pattern ‘‘a’’ and down-regulated in the others;
miR-149#was deregulated in pattern ‘‘b,’’ ‘‘c’’ and ‘‘d’’ HCCs
but not in pattern ‘‘a’’; miRNA let-7a# and miR-483-3p were
deregulated in pattern ‘‘c’’ and ‘‘d’’ HCCs. A recent study
correlated miR-149# up-regulation with the inhibition of
tumor neoangiogenesis in vitro and in murine model [36], and
miR-149# down-regulation with HCC aggressiveness [7].
miRNA let-7a# is an inhibitor of tumorigenicity via negative
RAS regulation [37, 38] and a suppressor of HCC invasion
in vitro [39]. miRNA 483-3p is known for his pro-apoptotic
function and was associated with HCC recurrence [15, 40].
The present study confirms the crucial role of the choice of
controls in the miRNA analyses: standard RNA controls—
based on hepatotrophic viruses, pre-cancerous lesions,
inflammatory activity, etc—are required to allow the com-
parison among different laboratories for the routine practice.
A short-term application might be the identification of
miRNAs as marker of HCC with higher aggressiveness and
higher recurrence risk, on biopsy specimen or directly on
serum. Tissue miRNA deregulation is very important in the
study of HCC progression, as also confirmed by our data, and
a standardization will be a crucial achievement for the future.
Funding This work has been supported by the Programma di Ricerca
Regione-Universita 2010–2012, Regione Emilia-Romagna, bando
‘‘Ricerca Innovativa’’ (Professor L. Bolondi), project title ‘‘Innovative
approaches to the diagnosis and pharmacogenetic-based therapies of
primary hepatic tumors, peripheral B and T-cell lymphomas and
lymphoblastic leukaemias.’’ The funding body had no role in the
study design, in the collection, analysis, interpretation of data, in the
writing of the manuscript and in the decision to submit the manuscript
for publication.
Compliance with ethical standards
Conflict of interest Authors declare no conflicts of interest.
References
1. Zhou XD, Tang ZY, Yang BH, et al. Experience of 1000 patients
who underwent hepatectomy for small hepatocellular carcinoma.
Cancer. 2001;91:1479–1486.
2. Wiemer EA. The role of microRNAs in cancer: no small matter.
Eur J Cancer. 2007;43:1529–1544.
3. Villa E, Critelli R, Lei B, et al. Neoangiogenesis-related genes all
hallmarks of fast-growing hepatocellular carcinomas and worst
survival. Results from a prospective study. Gut. 2016;65:
861–869.
4. Vasuri F, Fittipaldi S, Giunchi F, et al. Facing the enigma of the
vascular network in hepatocellular carcinomas in cirrhotic and
non-cirrhotic livers. J Clin Pathol. 2015;69:102–108.
5. Wang B, Hsu SH, Wang X, et al. Reciprocal regulation of
microRNA-122 and c-Myc in hepatocellular cancer: role of E2F1
and transcription factor dimerization partner 2. Hepatology.
2014;59:555–566.
6. Meng F, Henson R, Wehbe-Janek H, Ghoshal K, Jacob ST, Patel
T. MicroRNA-21 regulates expression of the PTEN tumor sup-
pressor gene in human hepatocellular cancer. Gastroenterology.
2007;133:647–658.
7. Zhang J, Jin H, Liu H, et al. MiRNA-99a directly regulates AGO2
through translational repression in hepatocellular carcinoma.
Oncogenesis. 2014;3:e97.
8. Anwar SL, Albat C, Krech T, et al. Concordant hypermethylation
of intergenic microRNA genes in human hepatocellular carci-
noma as new diagnostic and prognostic marker. Int J Cancer.
2013;133:660–670.
9. Edmunds RC, McIntyre JK, Luckenbach JA, Baldwin DH,
Incardona JP. Toward enhanced MIQE compliance: reference
residual normalization of qPCR Gene Expression Data. J Biomol
Tech. 2014;25:54–60.
10. Bruckert G, Vivien D, Docagne F, Roussel BD. Normalization of
reverse transcription quantitative PCR data during ageing in
distinct cerebral structures. Mol Neurobiol. 2016;53:1540–1550.
11. Jacob F, Guertler R, Naim S, et al. Careful selection of reference
genes is required for reliable performance of RT-qPCR in human
normal and cancer cell lines. PLoS One. 2013;8:e59180.
12. Kirschner MB, van Zandwijk N, Reid G. Cell-free microRNAs:
potential biomarkers in need of standardized reporting. Front
Genet. 2013;4:56.
13. Murakami Y, Toyoda H, Tanahashi T, et al. Comprehensive
miRNA expression analysis in peripheral blood can diagnose
liver disease. PLoS ONE. 2012;7:e48366.
14. Budhu A, Jia HL, Forgues M, et al. Identification of metastasis-
related microRNAs in hepatocellular carcinoma. Hepatology.
2008;47:897–907.
15. Sato F, Hatano E, Kitamura K, et al. MicroRNA profile predicts
recurrence after resection in patients with hepatocellular carci-
noma within the Milan Criteria. PLoS ONE. 2011;6:e16435.
16. Huang YS, Dai Y, Yu XF, et al. Microarray analysis of micro-
RNA expression in hepatocellular carcinoma and non-tumorous
tissues without viral hepatitis. J Gastroenterol Hepatol. 2008;23:
87–94.
17. Jiang J, Gusev Y, Aderca I, et al. Association of microRNA
expression in hepatocellular carcinomas with hepatitis infection,
cirrhosis, and patient survival. Clin Cancer Res. 2008;14:
419–427.
18. Ohta K, Hoshino H, Wang J, et al. MicroRNA-93 activates
c-Met/PI3K/Akt pathway activity in hepatocellular carcinoma by
directly inhibiting PTEN and CDKN1A. Oncotarget. 2015;6:
3211–3224.
19. Visani M, de Biase D, Marucci G, Taccioli C, Baruzzi A, Pession
A. Definition of miRNAs expression profile in glioblastoma
samples: the relevance of non-neoplastic brain reference. PLoS
ONE. 2013;8:e55314.
20. Mestdagh P, Hartmann N, Baeriswyl L, et al. Evaluation of
quantitative miRNA expression platforms in the microRNA
quality control (miRQC) study. Nat Methods. 2014;11:809–815.
2406 Dig Dis Sci (2017) 62:2397–2407
123
21. Fittipaldi S, Vasuri F, Degiovanni A, et al. Nestin and WT1
expression in atheromathous plaque neovessels: association with
vulnerability. Histol Histopathol. 2014;29:1565–1573.
22. Shesh NR, Ray HE, Yuan X, Pan J, Hamid T, Prabhu SD. Sta-
tistical analysis of repeated microRNA high-throughput data with
application to human heart failure: a review of methodology.
Open Access Med Stat. 2012;2012:21–31.
23. Callegari E, Gramantieri L, Domenicali M, D’Abundo L, Sab-
bioni S, Negrini M. MicroRNAs in liver cancer: a model for
investigating pathogenesis and novel therapeutic approaches. Cell
Death Differ. 2015;22:46–57.
24. Pradervand S,Weber J, Thomas J, et al. Impact of normalization on
miRNA microarray expression profiling. RNA. 2009;15:493–501.
25. Dang Y, Luo D, Rong M, Chen G. Underexpression of miR-34a
in hepatocellular carcinoma and its contribution towards
enhancement of proliferating inhibitory effects of agents target-
ing c-MET. PLoS ONE. 2013;8:e61054.
26. Gougelet A, Sartor C, Bachelot L, et al. Antitumour activity of an
inhibitor of miR-34a in liver cancer with b-catenin-mutations.
Gut. 2015. doi:10.1136/gutjnl-2014-308969.
27. Cui X, Wu Y, Wang Z, Liu X, Wang S, Qin C. MicroRNA-34a
expression is predictive of recurrence after radiofrequency abla-
tion in early hepatocellular carcinoma. Tumour Biol. 2015;36:
3887–3893.
28. Xu D, He XX, Chang Y, Sun SZ, Xu CR, Lin JS. Downregulation
of MiR-93 expression reduces cell proliferation and clonogenicity
of HepG2 cells. Hepatogastroenterology. 2012;59:2367–2373.
29. Shi KQ, Lin Z, Chen XJ, et al. Hepatocellular carcinoma asso-
ciated microRNA expression signature: integrated bioinformatics
analysis, experimental validation and clinical significance. On-
cotarget. 2015;6:25093–25108.
30. Pineau P, Volinia S, McJunkin K, et al. miR-221 overexpression
contributes to liver tumorigenesis. Proc Natl Acad Sci USA.
2010;107:264–269.
31. Gramantieri L, Ferracin M, Fornari F, et al. Cyclin G1 is a target
of miR-122a, a microRNA frequently down-regulated in human
hepatocellular carcinoma. Cancer Res. 2007;67:6092–6099.
32. Ge W, Yu DC, Li QG, Chen X, Zhang CY, Ding YT. Expression
of serum miR-16, let-7f, and miR-21 in patients with hepato-
cellular carcinoma and their clinical significances. Clin Lab.
2014;60:427–434.
33. Shimizu S, Takehara T, Hikita H, et al. The let-7 family of
microRNAs inhibits Bcl-xL expression and potentiates sorafenib-
induced apoptosis in human hepatocellular carcinoma. J Hepatol.
2010;52:698–704.
34. Wojcicka A, Swierniak M, Kornasiewicz O, et al. Next genera-
tion sequencing reveals microRNA isoforms in liver cirrhosis and
hepatocellular carcinoma. Int J Biochem Cell Biol. 2014;53:
208–217.
35. Dai H, Kang B, Zuo D. Effect of miR-30a-5p on the proliferation,
apoptosis, invasion and migration of SMCC-7721 human hepa-
tocellular carcinoma cells. Zhonghua Gan Zang Bing Za Zhi.
2014;22:915–920.
36. Chamorro-Jorganes A, Araldi E, Rotllan N, Cirera-Salinas D,
Suarez Y. Autoregulation of glypican-1 by intronic microRNA-
149 fine tunes the angiogenic response to FGF2 in human
endothelial cells. J Cell Sci. 2014;127:1169–1178.
37. Johnson SM, Grosshans H, Shingara J, et al. RAS is regulated by
the let-7 microRNA family. Cell. 2005;120:635–647.
38. Salvi A, Abeni E, Portolani N, Barlati S, De Petro G. Human
hepatocellular carcinoma cell-specific miRNAs reveal the dif-
ferential expression of miR-24 and miR-27a in cirrhotic/non-
cirrhotic HCC. Int J Oncol. 2013;42:391–402.
39. Drakaki A, Hatziapostolou M, Polytarchou C, et al. Functional
microRNA high throughput screening reveals miR-9 as a central
regulator of liver oncogenesis by affecting the PPARA-CDH1
pathway. BMC Cancer. 2015;15:542.
40. Bertero T, Bourget-Ponzio I, Puissant A, et al. Tumor suppressor
function of miR-483-3p on squamous cell carcinomas due to its
pro-apoptotic properties. Cell Cycle. 2013;12:2183–2193.
Dig Dis Sci (2017) 62:2397–2407 2407
123
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