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Wang et al. BMC Genomics (2023) 24:417
https://doi.org/10.1186/s12864-023-09478-z BMC Genomics
*Correspondence:
Xiaoming Dai
fydaixiaoming@126.com
Zhu Zhu
zhuzhu027@gmail.com
Full list of author information is available at the end of the article
Abstract
Background Non-coding RNAs play important roles in liver regeneration; however, their functions and mechanisms
of action in the regeneration of fibrotic liver have not been elucidated. We aimed to clarify the expression patterns
and regulatory functions of lncRNAs, circRNAs, miRNAs, and mRNAs in the proliferative phase of fibrotic liver
regeneration.
Methods Based on a mouse model of liver fibrosis with 70% hepatectomy, whole-transcriptome profiling was
performed using high-throughput sequencing on samples collected at 0, 12, 24, 48, and 72 h after hepatectomy.
Hub genes were selected by weighted gene co-expression network analysis and subjected to enrichment analysis.
Integrated analysis was performed to reveal the interactions of differentially expressed (DE) lncRNAs, circRNAs,
miRNAs, and mRNAs, and to construct lncRNA–mRNA cis- and trans-regulatory networks and lncRNA/circRNA–
miRNA–mRNA ceRNA regulatory networks. Real-Time quantitative PCR was used to validate part of the ceRNA
network.
Results A total of 1,329 lncRNAs, 48 circRNAs, 167 miRNAs, and 6,458 mRNAs were differentially expressed, including
812 hub genes. Based on these DE RNAs, we examined several mechanisms of ncRNA regulatory networks, including
lncRNA cis and trans interactions, circRNA parental genes, and ceRNA pathways. We constructed a cis-regulatory
core network consisting of 64 lncRNA–mRNA pairs (53 DE lncRNAs and 58 hub genes), a trans-regulatory core
network consisting of 103 lncRNA–mRNA pairs (18 DE lncRNAs and 85 hub genes), a lncRNA–miRNA–mRNA ceRNA
core regulatory network (20 DE lncRNAs, 12 DE miRNAs, and 33 mRNAs), and a circRNA–miRNA–mRNA ceRNA core
regulatory network (5 DE circRNAs, 5 DE miRNAs, and 39 mRNAs).
Conclusions These results reveal the expression patterns of lncRNAs, circRNAs, miRNAs, and mRNAs in the
proliferative phase of fibrotic liver regeneration, as well as core regulatory networks of mRNAs and non-coding
RNAs underlying liver regeneration. The findings provide insights into molecular mechanisms that may be useful in
Integrated analysis of lncRNA/circRNA–
miRNA–mRNA in the proliferative phase
of liver regeneration in mice with liver brosis
Qian Wang1, Zhangtao Long2, Fengfeng Zhu2, Huajian Li2, Zhiqiang Xiang2, Hao Liang2, Yachen Wu2, Xiaoming Dai2*
and Zhu Zhu2,3*
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Page 2 of 12
Wang et al. BMC Genomics (2023) 24:417
Introduction
Liver brosis is a pathophysiological process character-
ized by abnormal proliferation of connective tissue in
the liver, which results from chronic liver injury caused
by viral hepatitis, alcoholic steatohepatitis, or non-alco-
holic steatohepatitis [1]. Chronic liver injury triggers an
inammatory response, leading to the death of hepa-
tocytes and the accumulation of extracellular matrix,
resulting in the formation of scar tissue that underlies
liver brosis [2–4]. Studies have shown that liver brosis
can aect the reserve function and regenerative capac-
ity of the liver [5–7]. Furthermore, liver brosis often
progresses to liver cirrhosis, liver failure, and liver can-
cer, seriously aecting the prognosis and life quality of
patients [8].
e liver is one of the most important organs and has
a variety of complex functions such as detoxication,
metabolism, biosynthesis, and immunity [9]. Addition-
ally, the liver exhibits remarkable regenerative capabili-
ties: in rodents with 70% hepatectomy, the residual liver
rapidly restored its original size and regained its physi-
ological functions [10, 11]. Liver regeneration is typically
divided into three phases: the priming phase (0–6h), the
proliferative phase (12–72 h), and the terminal phase
(96–168h) [12]. e proliferative phase, in which qui-
escent (G0 phase) hepatocytes enter the cell cycle (G1/S
phase) and initiate proliferation, plays a key role in liver
regeneration [13]. Liver regeneration involves a variety of
cells (liver parenchymal and mesenchymal cells) and mol-
ecules (cytokines, growth factors, and metabolites) and is
triggered by stimuli such as surgery and injury [14–16].
Non-coding RNAs (ncRNAs) are RNA molecules
that are not translated into proteins and that mainly
include microRNAs (miRNAs), long non-coding RNAs
(lncRNAs), and circular RNAs (circRNAs). miRNAs are
single-stranded RNA molecules of 21–25 nucleotides
in length that can inhibit the expression of messenger
RNAs (mRNAs) by specically binding to the 3′-untrans-
lated region (3′-UTR) [17]. e competing endogenous
RNA (ceRNA) hypothesis proposes an RNA interaction
mechanism by which gene expression can be regulated
by non-coding RNAs, such as lncRNAs and circRNAs,
through competitively binding to miRNAs [12]. lncRNAs
are RNA molecules of 200–100,000 nucleotides that
regulate gene expression through cis-regulation, trans-
regulation, and ceRNAs [18, 19]. Additionally, circRNAs
are closed-loop RNA molecules capable of regulating
gene expression through mechanisms such as regulation
of parental genes, ceRNA interactions, and interactions
with RNA-binding proteins [20, 21]. Studies have dem-
onstrated that ncRNAs, including miRNAs, lncRNAs,
and circRNAs, are crucial regulators of liver regeneration
[22–24]. However, their functions in brotic liver regen-
eration are poorly understood.
In this study, a mouse model of liver brosis with 70%
hepatectomy was established to identify the key mRNAs
and establish lncRNA/circRNA–miRNA–mRNA regu-
latory networks across the proliferative phase of brotic
liver regeneration. Whole-transcriptome proling was
performed using high-throughput sequencing. Hub
genes were selected by weighted gene co-expression net-
work analysis (WGCNA) and subjected to enrichment
analysis, and dierentially expressed (DE) lncRNAs, cir-
cRNAs, miRNAs, and mRNAs were subjected to corre-
lation analysis. ese ndings provide new insights into
the mechanisms of liver regeneration that could help
identify biomarkers and therapeutic targets to ameliorate
liver brosis.
Materials and methods
Establishment of a mouse model of liver brosis with 70%
hepatectomy
Healthy adult male C57BL/6J mice (8 weeks of age and
weighing 24–26 g) were purchased from Hunan SJA
Laboratory Animal (Changsha, China). e mice were
housed at the Experimental Animal Center of Univer-
sity of South China (20–25°C, 50–55% humidity, 12h
light/12h dark, standard chow, and free access to water
and food). Carbon tetrachloride in olive oil was injected
intraperitoneally at 5 ml/kg twice a week for 8 weeks.
irty mice underwent 70% hepatectomy at week 9 by
excision of the left lateral and middle lobes after intra-
peritoneal injection of l% pentobarbital sodium at 50mg/
kg [25]. e liver samples were xed in 4% paraformalde-
hyde for histopathological examination using hematoxy-
lin and eosin and Masson staining. To assess hepatocyte
proliferation, the sections were further processed for
immunohistochemistry with anti-Ki-67 antibody (1:100,
Abcam, United Kingdom). e right lateral lobes were
collected at 0, 12, 24, 48, and 72 h after hepatectomy
with six mice in each group. e samples were stored
at − 80°C within 30min following collection. All animal
experiments were performed in accordance with inter-
nationally recognized guidelines for the care and use of
laboratory animals and were approved by the Committee
for the Care and Use of Experimental Animals of Univer-
sity of South China.
developing new therapeutic approaches to ameliorate diseases that are characterized by liver fibrosis, which would
be beneficial for the prevention of liver failure and treatment of liver cancer.
Keywords Liver fibrosis, Liver regeneration, LncRNA, CircRNA, MiRNA, MRNA
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Wang et al. BMC Genomics (2023) 24:417
High-throughput RNA sequencing
Liver samples of mice with liver brosis were sent to
Novogene (Beijing, China) for high-throughput sequenc-
ing. Strand-specic libraries were generated using the
NEBNext® UltraTM RNA Library Prep Kit for Illumina®
(NEB, USA). Total RNA was extracted, and the purity,
integrity and concentration were evaluated by 1% agarose
gel electrophoresis and OD260/280 and OD260/230 measure-
ment (NanoPhotometer® spectrophotometer; Implen,
Germany). e Agilent 2100 bioanalyzer (Agilent Tech-
nologies, USA) was used to further verify the RNA integ-
rity and quality. Total RNA (1 µg) of each sample was
used for transcriptome sequencing, and double-stranded
cDNA was synthesized using fragmented rRNA-free
RNA as a template. e puried double-stranded cDNA
was subjected to end repair, poly-A addition, and adap-
tor ligation. Resulting cDNA fragments of 350–400 bp
were selected using AMPure XP beads (Beckman Coul-
ter, USA), and the second cDNA strand was degraded.
Finally, PCR amplication was performed to construct
the library, and the eective concentration (> 2 nM) was
measured by real-time quantitative PCR (RT-qPCR) to
ensure the quality.
e construction of miRNA libraries followed a simi-
lar, but slightly dierent procedure. e small RNA
libraries were generated using the NEBNext® Multiplex
Small RNA Library Prep Set for Illumina® (NEB, USA).
Briey, 2µg of total RNA from each sample was used to
prepare the miRNA libraries. e puried miRNA was
ligated with adaptors at the 3ˊ and 5ˊ ends, and the rst
cDNA strand was synthesized using reverse transcription
primers. After PCR amplication of the cDNA library,
the products were puried, and 140–160bp DNA frag-
ments were recovered. Finally, the quality of the library
was assessed using an Agilent 2100 bioanalyzer (eective
library concentration > 2 nM).
Quality-veried libraries were used for sequencing
with PE150 on an Illumina NovaSeq 6000 platform (Illu-
mina, USA) based on the eective concentration of the
library and the requirements of data output. Raw reads
were ltered using Perl 6 to remove reads with adaptors,
undetermined bases at a frequency of > 0.002, or > 50%
low-quality bases at one end, in order to ensure the qual-
ity and reliability of the sequencing data. e error rates
(Q20 and Q30) and GC content were determined using
Illumina Casava (v1.8) to obtain clean reads for subse-
quent analysis (Q20 > 95%, Q30 > 90%, GC = 48–52%).
e reference genome (GRCm39) and gene model
annotation les of lncRNAs and mRNAs were down-
loaded from the genome website (https://www.ncbi.nlm.
nih.gov/). Clean reads were aligned against the reference
genome using hisat (v2.0.5), and the mapped read count
was calculated using StringTie (v1.3.3). circRNAs were
identied by nd_circ (v1.0) [26] and CIRI (v2.0.5) [27].
Clean reads were mapped to the reference sequences in
miRBase (v22.0) using Bowtie (v2.0.6) to identify known
miRNA sequences. e sQuantier.pl script in MirDeep2
was used to quantify miRNAs and obtain read counts.
Identication of dierentially expressed (DE) RNA genes
To quantify the gene expression levels, the read counts
were normalized using the transcripts per million
method. Violin plots were used to visualize the overall
distributions of gene expression levels. e heatmap R
package (v1.0.12) was used to generate heatmaps.
e lncRNAs, circRNAs, miRNAs, and mRNAs with
dierential expression in the 12h, 24, 48 and 72h groups
relative to the 0h group were selected using the edge
package (v3.38.4) in R (v4.1.0) [28]. Read counts were
normalized using trimmed mean of M-values, and DE
genes were selected based on the negative binomial dis-
tribution test. For lncRNAs, miRNAs, and mRNAs, the
selection criteria were |log2 (Fold Change)| > log2 (1.5)
and p < 0.05 for DE genes; log2 (Fold Change) > log2
(1.5) for signicantly up-regulated genes; and log2 (Fold
Change) < − log2 (1.5) for signicantly down-regulated
genes. For circRNAs, the selection criteria were |log2
(Fold Change)| > log2 (1) and p < 0.05 for DE genes; log2
(Fold Change) > log2 (1) for signicantly up-regulated
genes; and log2 (Fold Change) < − log2 (1) for signicantly
down-regulated genes. Finally, the ggplot2 R package
(v3.0.4) was used to generate volcano plots.
Weighted gene co-expression network analysis
A weighted gene co-expression network was created
using the WGCNA R package (v1.71). e read count of
DE mRNA was normalized using the fragments per kilo-
base million method for clustering. A soft threshold was
determined after removing outliers. An adjacency matrix
was constructed and transformed into a topological over-
lap matrix. DE mRNAs were clustered and divided into
modules using the dynamic tree cutting algorithm. Mod-
ule–phase correlations were calculated, and the mod-
ules with the highest correlations were selected as key
modules.
Identication and functional enrichment of hub genes
e gene signicance (GS, indicative of the correla-
tion between the gene expression pattern and the trait/
regeneration phase), and the module membership (MM,
indicative of the correlation between the gene expres-
sion pattern and module eigengene) were calculated. e
correlations between GS and MM were analyzed for key
modules and presented in scatter plots. e mRNAs with
|MM| >0.8 and |GS| >0.5 in key modules were selected as
hub genes for further analysis.
e R package ClusterProlter (v4.4.4) was used for
Gene Ontology [29] and Kyoto Encyclopedia of Genes
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Wang et al. BMC Genomics (2023) 24:417
and Genomes [30–33] enrichment analysis of the hub
genes. GO enrichment analysis included biological pro-
cesses, cellular components, and molecular functions,
but only biological processes are shown in the present
study. Gene annotation was performed using the org.
Mm.eg.db R package (v3.15.0). e p-values in GO/
KEGG enrichment analysis were calculated based on
a hypergeometric distribution and corrected using the
false discovery rate (FDR), and p < 0.05 indicated signi-
cant enrichment. Finally, the ggplot2 R package (v3.0.4)
was used to visualize the top ten terms/pathways based
on the p-value.
lncRNA/circRNA–mRNA regulatory networks and
functional enrichment
Pearson correlation coecients (r) between DE lncRNAs
or DE circRNAs and their potential regulatory targets
were calculated using the Hmisc R package (v4.7-1). DE
mRNAs located within 100kb upstream and downstream
of DE lncRNAs on the same chromosome were selected
as potential cis-regulatory targets. e lncRNA–mRNA
and circRNA-mRNA pairs with |r| >0.6 were selected
as cis-regulatory pairs, and lncRNA–mRNA pairs with
|r| >0.9 were selected as trans-regulatory pairs. e
mRNAs were subjected to GO/KEGG enrichment analy-
sis, and the top ten terms/pathways for the cis-regulatory
and trans-regulatory pairs were visualized based on the
p-values. e regulatory pairs containing hub genes were
selected as key pairs, and corresponding lncRNA/cir-
cRNA–mRNA cis-regulatory and trans-regulatory core
networks were constructed.
Construction of ceRNA core networks
Starbase [34] and miRanda (v3.3a) were used to predict
the potential targets of DE lncRNA–DE miRNA and hub
gene–DE miRNA pairs; miRanda (v3.3a) was used to
predict the potential targets of DE circRNA–DE miRNA
pairs. lncRNA/circRNA–miRNA–mRNA (hub gene)
ceRNA regulatory networks were constructed accord-
ing to the following criteria: (1) one or more DE miRNA
binding sites shared by DE lncRNAs/DE circRNAs and
hub genes; (2) positive correlations between the expres-
sion of DE lncRNAs/DE circRNAs and hub genes sharing
the DE miRNA binding sites; and (3) negative correla-
tions between the expression of DE miRNAs, target DE
lncRNAs and hub genes. lncRNA–miRNA–mRNA and
circRNA–miRNA–mRNA ceRNA core regulatory net-
works were constructed based on the Spearman correla-
tion coecient (ρ < 0.05).
Real-time quantitative PCR
One lncRNA–miRNA–mRNA pathway and one cir-
cRNA–miRNA–mRNA pathway were randomly selected
from the ceRNA regulatory networks, and the mRNAs in
both pathways were detected by RT-qPCR for validation.
RNA was extracted using the TRIzol method, and cDNA
was reverse transcribed using total mRNA as a template.
PCR was performed using SYBR Green PCR Master Mix
and specic primers (Supplementary Table S1). Relative
gene expression was calculated using the 2−ΔΔCt method.
e read counts from high-throughput RNA sequenc-
ing were normalized using the transcripts per million
method to obtain the relative expression of RNA for
comparison with the RT-qPCR results. One-way analysis
of variance was used for comparisons between groups.
e Bonferroni method was used to adjust the p-value,
and the signicant level set at P < 0.05. e expression
patterns of ncRNAs and mRNAs in the two pathways
were assessed to determine whether they conformed to
the ceRNA hypothesis.
Results
Analysis of lncRNAs, circRNAs, miRNAs, and mRNAs that
are dierentially expressed during liver regeneration
To identify regulatory networks that underlie the prolif-
erative phase of brotic liver regeneration, we established
a mouse model of liver brosis with 70% hepatectomy.
Subsequent examination revealed that all mice exhibited
the pathological features of liver brosis (Supplementary
Fig.1). Total RNA was extracted from liver samples at
0, 12, 24, 48, and 72h for high-throughput sequencing
and quantitative analysis. After excluding genes with no
expression in 25% of samples, 3,706 lncRNAs, 185 cir-
cRNAs, 563 miRNAs, and 14,999 mRNAs were identied
(Supplementary Table S2), for which the expression pat-
terns are presented as violin plots (Fig.1A–D) and heat-
maps (Fig.1E–H).
Compared with the 0h group, the proliferation phase
groups (12–72 h) showed the following dierentially
expressed genes: 661 lncRNAs (352 up-regulated and
309 down-regulated), 19 circRNAs (9 and 10), 52 miR-
NAs (35 and 17), and 3,837 mRNAs (1,795 and 2,042) in
the 12h group; 667 lncRNAs (358 and 309), 19 circRNAs
(7 and 12), 78 miRNAs (65 and 13), and 3,664 mRNAs
(2,107 and 1,557) in the 24h group; 556 lncRNAs (283
and 273), 23 circRNAs (10 and 13), 56 miRNAs (39 and
17), and 3,498 mRNAs (1,978 and 1,520) in the 48 h
group; and 502 lncRNAs (274 and 228), 21 circRNAs
(9 and 12), 85 miRNAs (61 and 24), and 3,282 mRNAs
(1,982 and 1,300) in the 72h group (Supplementary Table
S3 and Supplementary Fig.2). e RNAs with dieren-
tial expression at any time point after 70% hepatectomy
were considered to be DE genes. Together, these included
1,329 lncRNAs, 48 circRNAs, 167 miRNAs, and 6,458
mRNAs (Fig. 1I–L), representing signicant changes in
the proliferative phase of liver regeneration in mice with
liver brosis.
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Wang et al. BMC Genomics (2023) 24:417
Identication of key modules and hub genes associated
with liver regeneration
To further evaluate patterns of expression of the DE
RNAs during liver regeneration, we performed weighted
gene co-expression network analysis (WGCNA). After
normalizing the read counts of 6,458 DE mRNAs, the
outlier sample D4 was removed (Fig. 2A), and the soft
threshold was set to 11 (Fig. 2B). e remaining DE
mRNAs were then divided into 7 modules based on their
expression patterns. ese included MEturquoise (2,289
Fig. 2 Weighted gene co-expression network analysis of differentially expressed genes during liver regeneration. A Clustering of genes that were diffen-
tially expressed in the 12 h, 24 h, 48 h, and 72 h groups as compared to the 0 h group. B Soft threshold power for topological analysis. C Cluster dendro-
gram. D Hierarchical clustering heatmap. E-F Scatter plot of correlations between module membership (MM) and gene significance (GS) in MEturquoise
(E) and MEblue (F). G-H Clustering heatmaps of DE hub genes in MEturquoise (G) and MEblue (H)
Fig. 1 Expression patterns of lncRNAs, circRNAs, miRNAs, and mRNAs during liver regeneration in mice with liver fibrosis. A-D Violin plots of lncRNA
expression (A), circRNA expression (B), miRNA expression (C), and mRNA expression (D). E-H Hierarchical clustering heatmap of lncRNAs (E), circRNAs
(F), miRNAs (G), and mRNAs (H). I-L Venn diagram of lncRNAs (I), circRNAs (J), miRNAs (K), and mRNAs (L). Panels A-H show the expression profiles in 0 h,
12 h, 24 h, 48 h, and 72 h groups of mice; and panels I-L show differentially expressed (DE) RNAs in the proliferation phase (12–72 h) groups relative to
the 0 h group
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Wang et al. BMC Genomics (2023) 24:417
DE mRNAs), MEblue (930), MEgreen (548), MEgray
(542), MEred (448), MEyellow (788), and MEbrown (832)
(Fig.2C and Supplementary Table S4). e correlations
between co-expression modules and each regeneration
phase were determined (Fig.2D), and the MEturquoise
(0h) and the MEblue (12h) with the highest correlations
were selected as key modules.
Next, we sought to select and functionally enrich hub
genes within the key modules. Among 3,219 DE mRNAs
within the two key modules, scatter plots showed sig-
nicant correlations between gene signicance (GS) and
module membership (MM) (Fig. 2E–F). A total of 812
hub genes were selected. is included 556 hub genes
in MEturquoise, of which 79 were signicantly up-reg-
ulated and 477 were signicantly down-regulated at
0h (Fig.2G). Additionally, 256 hub genes in MEblue, of
which 238 were signicantly up-regulated and 18 were
signicantly down-regulated at 12h (Fig.2H).
Functional enrichment of hub genes associated with liver
regeneration
To determine the function of the hub genes in ME tur-
quoise and MEblue, we performed GO and KEGG
enrichment analyses. For GO analysis, the up-regulated
hub genes in MEturquoise were mainly associated with
regulation of cell growth, extracellular matrix organiza-
tion, and extracellular structure organization (Fig. 3A);
and the down-regulated hub genes were primarily asso-
ciated with fatty acid metabolic process, small molecule
catabolic process, and cellular amino acid metabolic
process (Fig. 3B). For KEGG analysis, the up-regulated
hub genes in MEturquoise showed no signicant KEGG
enrichment, and the down-regulated hub genes were
enriched in valine, leucine and isoleucine degradation,
steroid hormone biosynthesis, and pentose and glucuro-
nide interconversions (Fig.3C).
For MEblue, GO enrichment analysis indicate that
the up-regulated hub genes were mainly associated with
response to endoplasmic reticulum stress, Golgi vesicle
transport, and endoplasmic reticulum to Golgi vesicle-
mediated transport (Fig. 3D). e down-regulated hub
Fig. 3 Pathway analysis of hub genes. A–C GO/KEGG enrichment analysis of hub genes in MEturquoise. A GO enrichment analysis of up-regulated hub
genes. B GO enrichment analysis of down-regulated hub genes. C KEGG enrichment analysis of down-regulated hub genes. D–F GO/KEGG enrichment
analysis of hub genes in MEblue. D GO enrichment analysis of up-regulated hub genes. E GO enrichment analysis of down-regulated hub genes. F KEGG
enrichment analysis of up-regulated hub genes
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Wang et al. BMC Genomics (2023) 24:417
genes were primarily associated with cholesterol meta-
bolic process, sterol metabolic process, and secondary
alcohol metabolic process (Fig.3E). In KEGG enrichment
analysis, the up-regulated hub genes in MEblue were
enriched in protein export, amino sugar and nucleotide
sugar metabolism, and fructose and mannose metabo-
lism (Fig.3F), and the down-regulated hub genes showed
no signicant KEGG enrichment. ese pathways are
consistent with increased metabolic proliferative activity
and extracellular matrix remodeling, both of which are
associated with liver regeneration.
Identication and functional enrichment of core lncRNA/
circRNA–mRNA regulatory pairs associated with liver
regeneration
Given the key regulatory roles of lncRNAs and circRNAs
in ceRNA core networks, we evaluated the relationship
between lncRNAs/cirRNAs and mRNAs in terms of
cis- and trans-regulation potential. For lncRNAs, a total
of 175 DE lncRNAs and 190 DE mRNAs were identi-
ed, which formulated 212 potential cis-regulatory pairs
and 103 core trans-regulatory pairs. Based on hub genes
and their corresponding lncRNAs, 64 core cis-regulatory
pairs consisting of 53 DE lncRNAs and 58 hub genes were
identied (Fig. 4A). GO analysis revealed enrichment
in steroid metabolic process, ribonucleotide metabolic
process, and purine ribonucleotide metabolic process
(Supplementary Fig.3A); KEGG analysis showed enrich-
ment in steroid hormone biosynthesis, bile secretion,
and ascorbate and aldarate metabolism (Supplementary
Fig.3B). e 103 core trans-regulatory pairs consisted of
18 DE lncRNAs and 85 hub genes (Fig.4B). GO analysis
showed enrichment in small molecule catabolic process,
organic acid catabolic process, and steroid metabolic
process (Supplementary Fig.3C). KEGG analysis showed
enrichment in valine, leucine and isoleucine degradation,
fatty acid degradation, and tryptophan metabolism (Sup-
plementary Fig.3D).
For circRNAs, a total of 48 potential circRNA–parent
gene pairs were identied. GO analysis of parent genes
showed enrichment in small molecule catabolic process,
deoxyribonucleotide metabolic process, and nucleoside
metabolic process (Supplementary Fig.3E), while KEGG
analysis showed no signicantly enriched pathways.
Construction of ceRNA core networks
Next, we evaluated functional interactions based on the
set of 167 DE miRNAs that were dierentially expressed
in the 12h, 24h, 48h, or 72h group as compared to the
0h group. e Starbase database was used to predict the
targets of the DE miRNAs, which included 199,946 DE
miRNA–DE mRNA pairs and 1,456 DE miRNA–lncRNA
pairs (Supplementary Table S5). Additionally, miRanda
software was used to identify 29,237 DE miRNA–DE
mRNA pairs; 643,159 DE miRNA–lncRNA pairs; and
76,407 DE circRNA–DE miRNA pairs (Supplementary
Table S6). Based on the identied hub genes and the
ceRNA hypothesis, we constructed a lncRNA–miRNA–
mRNA ceRNA core regulatory network (Fig.5A), which
contained 20 lncRNAs, 12 miRNAs, and 33 hub genes.
We also constructed a circRNA–miRNA–mRNA ceRNA
core regulatory network (Fig.5B), which contained 5 cir-
cRNAs, 5 miRNAs, and 39 hub genes.
As verication of the ceRNA core networks, we ran-
domly selected RNAs in two pathways that are regu-
lated in response to liver regeneration for RT-qPCR
analysis. is includes lncRNA-Xist (NR_001463.3)/miR-
144-3P/mRNA-Aplp2, which was identied to be
downregulated, and circRNA-0000117/miRNA-
204-5p/mRNA-Derl1, which was identied to be upreg-
ulated in response to liver regeneration. eir relative
expression levels measured by RT-qPCR corresponded
with the results of high-throughput RNA sequencing,
and the change in the patterns of ncRNAs and mRNAs in
both pathways, which was most obvious at 12h but was
also showed a similar trend at other time points, con-
formed to the ceRNA hypothesis (Fig.6).
Discussion
Current transcriptome-wide studies on liver regeneration
have focused on healthy livers [25, 35]. is study pres-
ents the rst analysis of expression patterns and mecha-
nisms of action of lncRNAs, circRNAs, miRNAs, and
mRNAs in the regeneration of brotic liver, which may
provide new insights for the development of therapeutic
strategies for liver diseases. Liver brosis is a common
pathological process that occurs in a variety of chronic
liver diseases [36], and the injury stimuli and molecu-
lar signals are likely to be dierent for liver brosis and
regeneration. erefore, we reasoned that the transcrip-
tome-wide study of liver regeneration in the presence of
brosis may help to elucidate the molecular mechanisms
underlying curative processes in the disease and provide
new targets for preclinical investigations.
In this study, we employed WGCNA, a systems biology
approach for describing the correlation patterns among
genes [37], to eectively screen for hub genes closely
associated with phenotypes. MEturquoise (0 h) and
MEblue (12h) had the highest correlations with brotic
liver regeneration, suggesting that the expression of hub
genes exhibited the most signicant changes in the early
stage of liver regeneration. Using an ischemia-reperfusion
85% hepatectomy mouse model and mRNA microarray
analysis, Liu et al. [38] performed WGCNA and showed
that the changes of hub gene expression were most sig-
nicant during 6–24h after hepatectomy. Furthermore,
Zhu [39] conducted WGCNA based on a rat portal vein
ligation model and transcriptome-wide sequencing and
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Wang et al. BMC Genomics (2023) 24:417
revealed that the changes in hub gene expression were
most signicant at 0h after operation. ese ndings are
consistent with and further support our results.
We also performed a series of functional enrichment
analyses based on bioinformatic knowledge from GO and
KEGG pathway databases. e results demonstrated that
the up-regulated hub genes were mainly associated with
biological processes, such as regulation of cell growth,
protein and nucleotide processing and synthesis, and
extracellular matrix organization, which is consistent
with liver regeneration being primarily driven by hepa-
tocyte proliferation [40]. Cellular proliferation requires
a signicant amount of nucleotides for the synthesis of
RNAs involved in transcription and translation [41], and
regulatory proteins such as cytokines and growth factors
also play crucial roles [42]. Moreover, the extracellular
matrix provides a framework for hepatocytes and main-
tains homeostasis in the liver [43]. Over-deposition of
diuse extracellular matrix is a key feature of liver bro-
sis, and extracellular matrix degradation and remodeling
are important steps in liver regeneration [44]. e down-
regulated hub genes in this study were mainly enriched
in biological processes such as fatty acid metabolic pro-
cess, cholesterol metabolic process, steroid hormone
Fig. 4 Identification of lncRNA-mRNA and circRNA-mRNA core pairs. A lncRNA–mRNA cis-regulatory core network. B lncRNA–mRNA trans-regulatory
core network. Red, lncRNA; turquoise, hub genes in MEturquoise; and blue, hub genes in MEblue.
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Wang et al. BMC Genomics (2023) 24:417
biosynthesis, and branched-chain amino acid degrada-
tion. Lipid metabolism has an important role in liver
regeneration [42, 45]. Indeed, fatty acid oxidation is the
main source of energy for liver regeneration, and inhibi-
tion of fatty acid β-oxidation has been reported to delay
liver regeneration after hepatectomy [46]. Cholesterol
aects liver regeneration by regulating cell cycle progres-
sion [47], and steroids inhibit liver regeneration by sup-
pressing TNF-α and IL-6 overproduction and hepatocyte
DNA synthesis [48, 49]. Moreover, amino acids also have
a crucial role in liver regeneration. Valine, leucine, and
isoleucine, which are collectively known as branched-
chain amino acids, can promote liver regeneration by
enhancing hepatocyte growth factor secretion and pro-
tein synthesis [50, 51]. erefore, our ndings are con-
sistent with processes that are known to underlie liver
regeneration.
In the present study, lncRNA/circRNA–miRNA–
mRNA regulatory networks were constructed by
investigating the mechanisms of lncRNA cis and trans
interactions, circRNA parental genes, and ceRNA inter-
actions. Compared to existing transcriptome-wide stud-
ies on liver regeneration, this work provides a more
comprehensive analysis of the mechanisms of action
of ncRNAs [52, 53]. Among the lncRNA/circRNA–
miRNA–mRNA ceRNA core regulatory networks identi-
ed in our study, we selected two pathways for RT-qPCR
validation: lncRNA-Xist/miR-144-3P/mRNA-Aplp2,
which was downregulated by liver regeneration; and cir-
cRNA-0000117/miRNA-204-5p/mRNA-Derl1, which was
upregulated. Our results show that the relative expression
levels of the RNAs in these pathways were highly consis-
tent with the results of high-throughput RNA sequenc-
ing. Notably, the change patterns of ncRNAs and mRNAs
in the two pathways conformed to the ceRNA hypoth-
esis, which further arms the reliability of our analysis.
Studies have shown that lncRNA-XIST promotes the
proliferation of pancreatic and lung cancer cells by tar-
geting and inhibiting miR-133a and miR-144-3p, respec-
tively [54, 55]. Moreover, circ-0000117 promotes the
Fig. 5 Identification of ceRNA core regulatory networks. (A) lncRNA–miRNA–mRNA core network. (B) circRNA–miRNA–mRNA core network. Red, ln-
cRNA/circRNA; yellow, miRNA; turquoise, hub genes in MEturquoise; blue, hub genes in MEblue.
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Wang et al. BMC Genomics (2023) 24:417
proliferation of gastric cancer cells by inhibiting the miR-
337-3p/STAT3 axis [56]. Studies have also demonstrated
that miR-204 down-regulates the expression of Bcl-2,
Sirt1, and Fn-1 to inhibit the proliferation and promote
the apoptosis of hepatoma cells and tumor endothelial
cells [57, 58]. Taken together, we speculate that the above
pathways also have important roles in liver regeneration
by regulating cell proliferation and apoptosis.
ere are some limitations in our study. First, liver
brosis can arise from a range of chronic liver diseases,
such as viral hepatitis, alcoholic hepatitis, non-alcoholic
fatty liver, and autoimmune hepatitis. While our model
demonstrated consistent liver brosis features, it is inca-
pable of simulating all pathological alterations resulting
from dierent diseases. Second, although we examined
several mechanisms of ncRNA regulatory networks, such
as lncRNA cis and trans interactions, circRNA paren-
tal genes, and ceRNA pathways, there is still a need for
further exploration of additional mechanisms, includ-
ing protein binding and interaction, due to the intricate
nature of ncRNA mechanisms. Finally, through bioin-
formatics analysis, we predicted the regulatory networks
of ncRNAs and hub genes during the proliferative phase
of liver regeneration. However, the functions of the core
networks and pathways across the time spectrum need to
be experimentally veried in the future.
Conclusions
In this study, we revealed the expression patterns of
lncRNAs, circRNAs, miRNAs, and mRNAs in the pro-
liferative phase of liver regeneration in mice with liver
brosis. We identied hub mRNAs and constructed
lncRNA/circRNA–miRNA–mRNA regulatory net-
works. is study contributes to the understanding of the
molecular mechanisms of brotic liver regeneration. Our
ndings provide new insights into the process of liver
regeneration and potential targets for preclinical studies.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12864-023-09478-z.
Additional le 1: Supplementary Table S1. Sequences of PCR primers.
Additional le 2: Supplementary Table S2. Summary of all lncRNAs,
circRNAs, miRNAs, and mRNAs identified in this study.
Additional le 3: Supplementary Table S3. Summary of all DE lncRNAs,
DE circRNAs, DE miRNA and DE mRNAs identified in this study.
Additional le 4: Supplementary Table S4. DE mRNAs of each WGCNA
modules.
Additional le 5: Supplementary Table S5. miRNA-RNA targets pre-
dicted by Starbase.
Additional le 6: Supplementary Table S6. miRNA-RNA targets pre-
dicted by miRanda.
Additional le 7: Supplementary Fig. S1. Gross observation, Microscop-
ic observation of liver from mice with liver fibrosis A Gross observation of
liver from mice with liver fibrosis. B Hematoxylin and eosin staining (×100).
C Masson staining (×100). D-F Ki-67 immunohistochemistry at 0 h (D), 12 h
(E), and 72 h (F) after hepatectomy.
Additional le 8: Supplementary Fig. S2. Volcano plots of differentially
expressed genes in the proliferative phase of liver regeneration.
Additional le 9: Supplementary Fig. S3. Functional analysis of lncRNA-
mRNA and circRNA-mRNA core pairs. A GO enrichment analysis of lncRNA
cis-regulatory targets. B KEGG enrichment analysis of lncRNA cis-regulatory
targets C GO enrichment analysis of lncRNA trans-regulatory targets. D
Fig. 6 Releative expression levels of lncRNAs, circRNAs, miRNAs, and mRNAs in ceRNA networks. The black bar shows the qRT-PCR data and the the gray
bar shows the RNA-seq data. *P < 0.05 and **P < 0.01 for comparisons between groups
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 11 of 12
Wang et al. BMC Genomics (2023) 24:417
KEGG enrichment analysis of lncRNA trans-regulatory targets. E GO enrich-
ment analysis of circRNA parental genes.
Acknowledgements
Not applicable.
Authors’ contributions
Qian Wang performed the research. Fengfeng Zhu, Huajian Li, Zhiqiang Xiang,
Hao Liang, Yachen Wu analyzed the data. Zhangtao Long, Huajian Li wrote the
manuscript. Xiaoming Dai, Zhu Zhu designed the study. All authors approved
the manuscript.
Funding
This study was funded by Natural Science Foundation of Hunan Province
(Grant No. 2021JJ70120, Grant No. 2022JJ70119) and Clinical Medicine
Technological Innovation Leading Project of Hunan Province (Grant No.
2020SK51818).
Data availability
The datasets supporting the conclusions of this article are included within
the article and its additional files. The transcriptome sequencing data publicly
available at BioProject database under the BioProject ID PRJNA953495.
Declarations
Ethics approval and consent to participate
All of the animal procedures were performed in accordance with the Guide for
the Care and Use of Laboratory Animals of the People’s Republic of China and
approved by the Animal Ethics Committee of University of south china. This
study was carried out in compliance with the ARRIVE guidelines.
Consent for publication
Not applicable.
Competing interest
The authors declare that they have no competing interests.
Author details
1The First Affiliated Hospital, Department of Reproductive Medicine,
Hengyang Medical School, University of South China, Hengyang,
Hunan 421001, China
2The First Affiliated Hospital, Department of Hepatobiliary Surgery,
Hengyang Medical School, University of South China, Hengyang,
Hunan 421001, China
3The First Affiliated Hospital, Department of Education and Training,
Hengyang Medical School, University of South China, Hengyang,
Hunan 421001, China
Received: 24 April 2023 / Accepted: 22 June 2023
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