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. 2019 Feb 5;19(4):2649–2659. doi: 10.3892/mmr.2019.9931

Comprehensive bioinformatics analysis of critical lncRNAs, mRNAs and miRNAs in non-alcoholic fatty liver disease

Huiling Wu 1,*, Xi Song 1,*, Yuntao Ling 2, Jin Zhou 3, Zhen Tao 2, Yuying Shen 1,✉
PMCID: PMC6423652  PMID: 30720100

Abstract

Non-alcoholic fatty liver disease (NAFLD) is the most common fatty liver disease in developed countries, in which fat accumulation in the liver is induced by non-alcoholic factors. The present study was conducted to identify NAFLD-associated long non-coding RNAs (lncRNAs), mRNAs and microRNAs (miRNAs). The microarray dataset GSE72756, which included 5 NAFLD liver tissues and 5 controls, was acquired from the Gene Expression Omnibus database. Differentially expressed lncRNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) were detected using the pheatmap package. Using the clusterProfiler package and Cytoscape software, enrichment and protein-protein interaction (PPI) network analyses were conducted to evaluate the DE-mRNAs. Next, the miRNA-lncRNA-mRNA interaction network was visualized using Cytoscape software. Additionally, RP11-279F6.1 and AC004540.4 expression levels were analyzed by reverse transcription quantitative polymerase chain reaction. There were 318 DE-lncRNAs and 609 DE-mRNAs identified in the NAFLD tissues compared with the normal tissues. Jun proto-oncogene, AP-1 transcription factor subunit (JUN), which is regulated by AC004540.4 and RP11-279F6.1, exhibited higher degree compared with other nodes in the PPI network. Furthermore, miR-409-3p and miR-139 (targeting JUN) were predicted as PPI network nodes. In the miRNA-lncRNA-mRNA network, miR-20a and B-cell lymphoma 2-like 11 (BCL2L11) were among the top 10 nodes. Additionally, BCL2L11, AC004540.4 and RP11-279F6.1 were targeted by miR-20a, miR-409-3p and miR-139 in the miRNA-lncRNA-mRNA network, respectively. RP11-279F6.1 and AC004540.4 expression was markedly enhanced in NAFLD liver tissues. These key RNAs may be involved in the pathogenic mechanisms of NAFLD.

Keywords: non-alcoholic fatty liver disease, long non-coding RNA, mRNA, microRNA, regulatory network

Introduction

As the most frequently diagnosed fatty liver disease in developed countries, non-alcoholic fatty liver disease (NAFLD) occurs when fat is enriched in the liver due to non-alcoholic factors (1,2). The risk factors for NAFLD include metabolic syndromes, for example combined hyperlipidemia, obesity, high blood pressure and type II diabetes mellitus and insulin resistance (3,4). Non-alcoholic steatohepatitis (NASH), which is the most severe type of NAFLD, is considered to be the primary cause of cirrhosis (4). The incidence of NAFLD is 9–36.9% globally (5,6), and ~20% of people in the United States of America (75–100 million people) are affected by the disease (7). Therefore, exploring the pathogenic mechanisms of NAFLD and developing novel treatment protocols are necessary.

Silencing of fatty acid transport protein 5 (FATP5) may reverse NAFLD; therefore, the activity of hepatic FATP5 is considered critical for maintaining fatty acid flux and caloric uptake during high-fat feeding (8). Patatin-like phospholipase domain containing 3 is an NAFLD-associated gene, which is closely associated with metabolic changes in hepatocytes and lipogenesis (9,10). Interleukin-17 is associated with the proinflammatory response and hepatic steatosis in NAFLD and contributes to the steatosis-steatohepatitis transition (11). Several microRNAs (miRNAs), including miR-21, miR-122, miR-451 and miR-34a, are overexpressed in patients with NAFLD; in particular, miR-122 levels are associated with the grades of fatty liver disease and are a potential marker of NAFLD (12). By inhibiting the expression of 3-hydroxy-3-methylglutaryl-co-enzyme A reductase, miR-21 was demonstrated to mediate cholesterol and triglyceride metabolism in an NAFLD model and may be a promising diagnostic and therapeutic marker for the disease (13). miR-34a/Sirtuin 1/tumor protein p53 (p53) signaling, which is correlated with liver cell apoptosis, is inhibited by ursodeoxycholic acid and activated upon the aggravation of illness in NAFLD (14). Despite these studies, the molecular mechanisms of NAFLD are not fully understood.

In 2015, Sun et al (15) investigated the role of long non-coding RNAs (lncRNAs) in NAFLD through microarray data analysis and identified that several differentially expressed lncRNAs (DE-lncRNAs) function in the pathogenesis of NAFLD. However, the molecular regulatory mechanisms in NAFLD have not been explored in detail. Based on the expression profiles deposited by Sun et al (15), the present study additionally identified the DE-lncRNAs and differentially expressed mRNAs (DE-mRNAs) between NAFLD and normal liver tissues. In addition, key mRNAs, lncRNAs and miRNAs involved in NAFLD were also identified through protein-protein interaction (PPI) network, enrichment and miRNA-lncRNA-mRNA interaction network analyses. The expression of key RNAs were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Materials and methods

Data source

The normalized expression data and annotation data from the GSE72756 dataset were acquired from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), which was generated using a GPL16956 Agilent-045997 Arraystar human lncRNA microarray V3 (Probe Name Version) platform. The sample set of GSE72756 included 5 NAFLD liver tissues (3 females and 2 males; mean age=38.8 years) and 5 normal liver tissues (3 females and 2 males; mean age=39.2 years). Samples used in this dataset were sourced from patients with NAFLD without other metabolic complications that were hospitalized in The Third Xiangya Hospital (Changsha, China) from March 2014 to November 2014, and NAFLD was confirmed independently by two senior pathologists by pathological examination. Liver tissues (50–100 mg) were isolated from the patients and then rapidly frozen in liquid nitrogen. Sun et al (15) deposited the microarray dataset GSE72756, and the study was approved by the Ethics Committee of the Third Xiangya Hospital of Central South University. Informed consent was obtained from all patients.

Differential expression analysis

Based on the normalized probe expression data, the R package in Linear Models for Microarray Data (16) (http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) was utilized to identify and annotate the differentially expressed probes between NAFLD and normal liver tissues. A false discovery rate (FDR; adjusted P-value) <0.01 and |log fold-change (FC)|>1 were set as thresholds. Using the R package pheatmap (17) (http://cran.r-project.org/web/packages/pheatmap/index.html), a clustering heatmap was drawn for differentially expressed probes. According to the annotation information, the differentially expressed probes were divided into DE-lncRNAs and DE-mRNAs.

Enrichment analysis and PPI network analysis of DE-mRNAs

Using the R package clusterProfiler (18) (http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html), Gene Ontology (GO; http://www.geneontology.org) (19) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.ad.jp/kegg) (20) pathway enrichment analyses were performed for the DE-mRNAs. The terms with FDR <0.05 were considered to be significant. Based on the Search Tool for the Retrieval of Interacting Genes (http://www.string-db.org/) (21) database, the interaction pairs among the DE-mRNAs were predicted with the threshold of required confidence >0.4. The PPI network was constructed using Cytoscape software (version 3.0.1, http://www.cytoscape.org) (22). Using the CytoNCA plugin (http://apps.cytoscape.org/apps/cytonca) (23) in Cytoscape, Closeness centrality (CC) (24), Degree Centrality (DC) (25), and Betweenness centrality (BC) (26) scores were calculated. The nodes with increased CC, DC and BC scores compared with other nodes were identified as hub nodes (27) in the PPI network.

Construction of the lncRNA-mRNA regulatory network and functional prediction of DE-lncRNAs

Based on the Pearson product-moment correlation coefficient (28), the target genes of upregulated and downregulated lncRNAs were predicted using a threshold of FDR <0.05 and correlation coefficient >0.995. Next, Cytoscape software (22) was utilized to draw lncRNA-mRNA regulatory networks. Additionally, enrichment analysis was conducted for the target genes of each DE-lncRNA using FDR <0.05 as a threshold. Using the R package clusterProfiler (18), the enriched pathways were compared to identify the significant pathways of target genes for each DE-lncRNA. An FDR <0.05 was used as the cut-off criterion.

Construction of miRNA-lncRNA-mRNA interaction network

Combined with the WEB-based gene set analysis toolkit (Webgestalt; http://www.webgestalt.org) (29), the miRNAs targeting DE-mRNAs involved in the PPI network were predicted. A count (number of target genes) ≥4 and FDR <0.05 were set as the thresholds. According to the annotation information of the DE-lncRNAs, their corresponding sequences were extracted from human reference genome hg19 (30). The mature sequences of the predicted miRNAs were downloaded from the miRbase database (http://www.mirbase.org/) (31). Using MiRanda (http://www.microrna.org) (32) and RNAhybrid (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid/) (33) software, lncRNAs containing significant binding sites for the aforementioned miRNAs were predicted. The predicted results of the miRanda and RNAhybrid analyses were compared to obtain the intersecting miRNA-lncRNA pairs. Based on the obtained miRNA-lncRNA and miRNA-mRNA pairs, and the associated lncRNA-mRNA and mRNA-mRNA pairs, an miRNA-lncRNA-mRNA interaction network was constructed and subjected to topological property analysis using Cytoscape software.

Sample information and RT-qPCR

A total of 2 normal liver tissues (2 females; age range: 47–60 years; mean age=53.5 years; obtained from March to June 2017 via surgical resection) and 2 NAFLD liver tissues (2 males; age range: 41–47 years; mean age=44 years; obtained from April to May 2017 via surgical resection) were provided by Nanjing First Hospital (Nanjing, China). The present study was approved by the Ethics Committee of Nanjing First Hospital. Informed consent was obtained from all patients.

Using TRIzol® reagent (Thermo Fisher Scientific, Inc., Waltham, MA, USA), total RNA was extracted from the samples. The PrimeScript RT Master MIX kit (Takara Bio, Inc., Otsu, Japan) was used to synthesize first-strand cDNA. RT-qPCR was performed using Power SYBR Green PCR Master Mix (cat. no., 4367659; Thermo Fisher Scientific, Inc.) on an ABI 7500 FAST real-time PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc.). The thermocycler conditions for qPCR were as follows: Initial denaturation (50°C, 3 min); 40 cycles of denaturation (95°C, 3 min), annealing (95°C, 10 sec), and extension (60°C, 30 sec). The specificity of the primer amplicons was examined by melting curve analysis. The comparative Cq method (34) was employed to quantify target mRNA and miRNA expression. mRNA expression was normalized to that of GAPDH. The primers used in the present study were as follows: GAPDH-forward (F)-5′-TGACAACTTTGGTATCGTGGAAGG-3′; GAPDH-reverse (R)-5′-AGGCAGGGATGATGTTCTGGAGAG-3′; RP11-279F6.1-h-F-5′-CGGACATAGCCAACGCACCT-3′; RP11-279F6.1-R-5′-TTCATACTTCTGCTGCGTCCA-3′; AC004540.4-F-5′-TTCACAACACACTCAAAGCCT-3′; AC004540.4-R-5′-CAACTGCACTCCAAATGGCTA-3′.

Statistical analysis

All data are expressed as the mean ± standard error of the mean, and the differences between the two groups were compared by Student's t-test. Statistical analyses were performed using SPSS 22.0 software (IBM Corp., Armonk, NY, USA), and GraphPad Prism 5 (GraphPad Software, Inc., La Jolla, CA, USA) was used to visualize the results. P<0.05 was considered to indicate a statistically significant difference.

Results

Differential expression analysis

The clustering heatmap for differentially expressed probes is presented in the Fig. 1. There were 318 DE-lncRNAs, including 105 upregulated and 213 downregulated, and 609 DE-mRNAs, including 353 upregulated and 256 downregulated, in the NAFLD liver tissues compared with the normal liver tissues.

Figure 1.

Figure 1.

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Clustering heatmap for the differentially expressed probes. GSM1860540, GSM1860541, GSM1860543, GSM1860545 and GSM1860548 represent normal liver tissue samples. GSM1860542, GSM1860544, GSM1860546, GSM1860547 and GSM1860549 represent non-alcoholic fatty liver disease liver tissue samples. The blue-yellow-red variation indicates alterations in expression from low to high. The y- and upper x-axes represent the cluster of genes and samples with similar alterations in expression, respectively.

Enrichment analysis and PPI network analysis of DE-mRNAs

The top 5 GO ‘biological process’ (BP), GO ‘cellular component’ (CC), GO ‘molecular function’ (MF) and KEGG terms enriched in the DE-mRNAs analysis are summarized in Table I. For the upregulated mRNAs, the enriched GO and KEGG terms primarily included ‘carboxylic acid metabolic process’ (GO_BP; FDR=1.24×10−20), ‘cytoplasmic part’ (GO_CC; FDR=8.66×10−12), ‘molecular function’ (GO_MF; FDR=1.73×10−14), and ‘metabolic pathways’ (KEGG pathway; FDR=2.65×10−09). Downregulated mRNAs were primarily enriched in ‘single-organism process’ (GO_BP; FDR=1.52×10−12), ‘extracellular space’ (GO_CC; FDR=5.95×10−11), ‘molecular function’ (GO_MF; FDR=6.89×10−09), and ‘adrenergic signaling in cardiomyocytes’ (KEGG pathway; FDR=4.34×10−2). The PPI network for the DE-mRNAs involved 442 nodes and 1,409 edges, and it was identified that Jun proto-oncogene, AP-1 transcription factor subunit (JUN) interacted with B-cell lymphoma 2 (Bcl-2)-like 11 (BCL2L11), as demonstrated in Fig. 2. The top 15 nodes, including JUN, with the highest BC, CC and DC scores are summarized in Table II.

Table I.

Top 5 functions and pathways enriched in the analysis of differentially expressed mRNAs.

Category Term ID Description P-value FDR Count
Upregulated GO_BP GO:0019752 Carboxylic acid metabolic process 2.90×10−24 1.24×10−20   69
GO_BP GO:0032787 Monocarboxylic acid metabolic process 7.56×10−24 1.61×10−20   53
GO_BP GO:0006082 Organic acid metabolic process 1.14×10−23 1.62×10−20   73
GO_BP GO:0043436 Oxoacid metabolic process 2.13×10−23 2.27×10−20   72
GO_BP GO:0044281 Small molecule metabolic process 7.36×10−22 6.27×10−19 110
GO_CC GO:0044444 Cytoplasmic part 1.98×10−14 8.66×10−12 206
GO_CC GO:0044432 Endoplasmic reticulum part 1.50×10−12 3.27×10−10   55
GO_CC GO:0005783 Endoplasmic reticulum 3.69×10−12 5.38×10−10   68
GO_CC GO:0044421 Extracellular region part 5.42×10−12 5.93×10−10 120
GO_CC GO:0005789 Endoplasmic reticulum membrane 1.20×10−11 1.05×10−9   48
GO_MF GO:0003674 Molecular function 2.54×10−17 1.73×10−14 324
GO_MF GO:0003824 Catalytic activity 1.18×10−15 4.01×10−13 163
GO_MF GO:0048037 Cofactor binding 4.10×10−12 9.30×10−10   25
GO_MF GO:0016491 Oxidoreductase activity 8.89×10−12 1.51×10−9   41
GO_MF GO:0050662 Coenzyme binding 2.34×10−8 3.18×10−6   17
KEGG Pathway hsa01100 Metabolic pathways 1.02×10−11 2.65×10−9   72
KEGG Pathway hsa00040 Pentose and glucuronate interconversions 2.24×10−5 2.59×10−3   7
KEGG Pathway hsa01200 Carbon metabolism 3.94×10−5 2.59×10−3   12
KEGG Pathway hsa00830 Retinol metabolism 4.32×10−5 2.59×10−3     9
KEGG Pathway hsa05204 Chemical carcinogenesis 4.99×10−5 2.59×10−3   10
Downregulated GO_BP GO:0044699 Single-organism process 4.23×10−16 1.52×10−12 197
GO_BP GO:0032501 Multicellular organismal process 1.48×10−12 1.85×10−9 128
GO_BP GO:0044707 Single-multicellular organism process 1.54×10−12 1.85×10−9 125
GO_BP GO:0008150 Biological process 6.60×10−12 5.93×10−9 210
GO_BP GO:0051128 Regulation of cellular component organization 1.24×10−11 8.92×10−9   58
GO_CC GO:0005615 Extracellular space 1.54×10−13 5.95×10−11   52
GO_CC GO:0044449 Contractile fiber part 1.04×10−11 2.02×10−9   19
GO_CC GO:0005576 Extracellular region 2.82×10−11 3.64×10−9 103
GO_CC GO:0043292 Contractile fiber 4.73×10−11 4.58×10−9   19
GO_CC GO:0030017 Sarcomere 1.73×10−10 1.34×10−8   17
GO_MF GO:0003674 Molecular function 1.33×10−11 6.89×10−9 213
GO_MF GO:0005515 Protein binding 8.48×10−9 2.19×10−6 159
GO_MF GO:0008307 Structural constituent of muscle 1.73×10−8 2.99×10−6     8
GO_MF GO:0008092 Cytoskeletal protein binding 3.61×10−8 4.67×10−6   29
GO_MF GO:0005488 Binding 1.29×10−6 1.34×10−6 187
Pathway hsa04261 Adrenergic signaling in cardiomyocytes 2.73×10−4 4.34×10°     8
Pathway hsa00512 Mucin type O-glycan biosynthesis 4.43×10−4 4.34×10°     4
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GO, Gene Ontology; BP, biological process; CC, cell component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

Figure 2.

Figure 2.

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Protein-protein interaction network of differentially expressed mRNAs. Red and green dots represent upregulated and downregulated mRNAs, respectively. Node size represents the degree of the node.

Table II.

Top 15 nodes with increased DC, BC and CC scores in the protein-protein interaction network.

DC BC CC
mRNA Score mRNA Score mRNA Score
JUN 59 JUN 33383.63 JUN 0.069956
ACACA 52 ACACA 23963.01 FOS 0.069231
FOS 51 CTNNB1 18141.12 CTNNB1 0.069133
CDK1 45 FOS 16604.96 DECR1 0.068756
DECR1 42 DECR1 15936.45 MAPK3 0.068542
CTNNB1 41 CDK1 12244.42 ACACA 0.068531
MAPK3 36 IL8 11466.93 ACTA1 0.068425
ACTA1 32 MAPK3 10861.69 ACTA2 0.068383
ENO3 31 ACTA1 9482.17 CREM 0.068351
CCNB1 31 GART   8161.233 CDK1 0.068277
IL8 29 MBP   7587.735 AR 0.068087
ACTA2 29 AR   6690.792 GART 0.068024
GART 28 ENO3   6230.197 IL8 0.068014
EHHADH 24 DCN   6073.046 CCNB1 0.067804
DCN 23 RTN4IP1   5927.582 ENO3 0.067784
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BC, Betweenness centrality; CC, Closeness centrality; DC, Degree Centrality.

Construction of the lncRNA-mRNA regulatory network and functional prediction of DE-lncRNAs

Following prediction of the target genes of the upregulated and downregulated lncRNAs, lncRNA-mRNA regulatory networks were constructed. For the upregulated lncRNAs, the lncRNA-mRNA regulatory network contained 182 nodes (including 37 lncRNAs and 145 target genes) and 672 interactions. For the downregulated lncRNAs, the lncRNA-mRNA regulatory network contained 140 nodes (including 47 lncRNAs and 93 target genes) and 450 interactions, among which AC004540.4 and RP11-279F6.1 were identified to target JUN. The top 10 nodes (including AC004540.4 and RP11-279F6.1) in the lncRNA-mRNA regulatory networks are summarized in Table III. Subsequent to enrichment analysis, the enriched pathways for the target genes of each upregulated and downregulated lncRNA were compared to identify significant pathways.

Table III.

Top 10 nodes in the lncRNA-mRNA regulatory networks for the upregulated and downregulated lncRNAs.

Upregulated Downregulated
Symbol Degree Symbol Degree
AC004540.4 60 XLOC_014103 47
RP11-279F6.1 54 C17orf76-AS1 45
AQP7P1 53 RP11-279F6.3 41
XLOC_007896 52 AK027145 41
AK025288 51 RP11-13L2.4 34
RP1-60O19.1 47 RP11-120K9.2 30
TRHDE-AS1 46 LOC100505806 29
RP11-345M22.2 42 BC073897 26
LOC100422737 38 RP11-471J12.1 21
AK055386 29 RMST 14
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lncRNA, long non-coding RNA.

Construction of miRNA-lncRNA-mRNA interaction network

The miRNAs (including miR-409-3p and miR-139, which targeted JUN) targeting the DE-mRNAs involved in the PPI network are summarized in Table IV. Subsequent to prediction of the binding sites between DE-lncRNAs and the miRNAs associated with the miRNA-mRNA pairs, an miRNA-lncRNA interaction network was constructed (involving 26 miRNAs, 111 lncRNAs and 224 interactions). In the miRNA-lncRNA interaction network, miR-409-3p and miR-139 interacted with AC004540.4 and RP11-279F6.1, respectively. The top 10 nodes exhibiting the highest degrees are summarized in Table V. Finally, an miRNA-lncRNA-mRNA interaction network was constructed, which contained 249 nodes, including 36 miRNAs, 95 lncRNAs, 118 mRNAs and 845 interactions (Fig. 3). The top 10 nodes [including miR-20a; solute carrier family 30, member 10, (SLC30A10); and BCL2L11] with the highest degrees in the miRNA-lncRNA-mRNA interaction network are summarized in Table VI. In particular, BCL2L11 was targeted by miR-20a in the interaction network.

Table IV.

miRNAs targeting the differentially expressed mRNAs involved in the protein-protein interaction network.

miRNA Gene count P-value
hsa_CAGTATT, miR-200B, miR-200C, miR-429 19 adjP=0.00004
hsa_ACCAAAG, miR-9 16 adjP=0.00220
hsa_AACATTC, miR-409-3P   8 adjP=0.00220
hsa_GGCAGCT, miR-22 10 adjP=0.00220
hsa_ACTGTAG, miR-139   7 adjP=0.00240
hsa_ACATTCC, miR-1, miR-206 11 adjP=0.00240
hsa_AAAGGAT, miR-501   7 adjP=0.00240
hsa_TGTTTAC, miR-30A-5P, miR-30C, miR-30D, miR-30B, miR-30E-5P 16 adjP=0.00240
hsa_GTGCCTT, miR-506 19 adjP=0.00240
hsa_TACTTGA, miR-26A, miR-26B 11 adjP=0.00240
hsa_ACTTTAT, miR-142-5P 11 adjP=0.00240
hsa_GTGCCAT, miR-183   8 adjP=0.00360
hsa_TGGTGCT, miR-29A, miR-29B, miR-29C 14 adjP=0.00670
hsa_ACTGTGA, miR-27A, miR-27B 13 adjP=0.00680
hsa_GCACTTT, miR-17-5P, miR-20A, miR-106A, miR-106B, miR-20B, miR-519D 15 adjP=0.00700
hsa_GTGCAAT, miR-25, miR-32, miR-92, miR-363, miR-367 10 adjP=0.00760
hsa_TCTGATC, miR-383   4 adjP=0.00820
hsa_CCTGTGA, miR-513   6 adjP=0.00920
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hsa, Homo sapiens; miR, microRNA; adjP, adjusted P-value.

Table V.

Top 10 nodes with highest degrees in the miRNA-lncRNA interaction network.

miRNA lncRNA
Symbol Degree Symbol Degree
hsa-miR-92a 32 C17orf76-AS1 9
hsa-miR-367 21 AC079776.2 8
hsa-miR-20b 21 RP4-669L17.4 6
hsa-miR-17-5p 21 XLOC_001223 5
hsa-miR-145 18 XLOC_000638 5
hsa-miR-25 15 RP11-365O16.3 5
hsa-miR-20a 15 RP11-315I20.1 5
hsa-miR-106a 11 RP11-261C10.3 5
hsa-miR-501 10 RP11-249C24.3 5
hsa-miR-106b   9 BC005927 5
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hsa, Homo sapiens; miRNA/miR, microRNA; lncRNA, long non-coding RNA.

Figure 3.

Figure 3.

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miRNA-lncRNA-mRNA interaction network. Orange diamonds, pink triangles and green circles represent lncRNAs, miRNAs and mRNAs, respectively. Node size represents the degree of the node. lncRNA, long non-coding RNA; miRNA, microRNA.

Table VI.

Top 10 nodes with highest degrees in the miRNA-lncRNA-mRNA interaction network.

miRNA lncRNA mRNA
Symbol Degree Symbol Degree Symbol Degree
hsa-miR-92 42 AQP7P1 15 FOS 25
hsa-miR-20b 36 C17orf76-AS1 14 JUN 24
hsa-miR-17-5p 36 AK055386   8 BCL2L11 20
hsa-miR-367 31 RP11-365O16.3   7 ACACA 19
hsa-miR-20a 30 AC079776.2   7 STK39 17
hsa-miR-106a 26 SLC30A10   6 SLC38A2 15
hsa-miR-25 25 RP4-669L17.4   6 CDK16 15
hsa-miR-106b 24 XLOC_014103   5 GFPT2 14
hsa-miR-506 21 XLOC_000638   5 PTHLH 14
hsa-miR-200c 20 XLOC_001223   5 RPS6KA2 13
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Hsa, Homo sapiens; miRNA/miR, microRNA; lncRNA, long non-coding RNA.

RP11-279F6.1 and AC004540.4 expression

The RT-qPCR results revealed that RP11-279F6.1 and AC004540.4 expression levels were markedly enhanced in the liver tissues from patients with NAFLD compared with the control liver samples (Fig. 4; P<0.01).

Figure 4.

Figure 4.

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Expression levels of (A) RP11-279F6.1 and (B) AC004540.4 in the control and NAFLD liver tissues by reverse transcription quantitative polymerase chain reaction. ***P<0.01 vs. control. NAFLD, non-alcoholic fatty liver disease.

Discussion

In the present study, 318 DE-lncRNAs, including 105 upregulated and 213 downregulated lncRNAs, and 609 DE-mRNAs, including 353 upregulated and 256 downregulated mRNAs, were screened in the NAFLD liver tissues compared with normal liver tissues. In the PPI network for DE-mRNAs, JUN, the targeting gene of BCL2L11, was among the top 15 nodes. JUN was targeted by the lncRNAs RP11-279F6.1 and AC004540.4 and miRNAs miR-409-3p and miR-139. Additionally, miR-409-3p and miR-139 were predicted as the DE-mRNAs involved in the PPI network. Additionally, miR-409-3p and miR-139 were regulated by AC004540.4 and RP11-279F6.1, respectively. In the miRNA-lncRNA-mRNA interaction network, miR-20a, SLC30A10 and BCL2L11 were among the top 10 nodes. RP11-279F6.1 and AC004540.4 expression was markedly increased in the NAFLD patient liver tissues compared with the control liver samples.

JUN, also known as AP1, was identified to be increased in a previous study of NAFLD (35). Phosphorylation of the transcriptional activation domain of AP1 is conducted by JNKs to enhance its activity, thereby accelerating the progression and development of NASH (36,37). JUN is considered to be an oncogene in the liver, and its expression is enhanced in response to inflammatory stimuli, promoting liver tumorigenesis (38,39). JUN serves an important role in hepatitis B virus-associated tumorigenesis by promoting the proliferation of hepatocytes and dysplasia progression, indicating that JUN is a useful treatment target for preventing hepatitis-associated tumorigenesis (40). The results from the present study indicated that JUN is involved in the pathogenesis of NAFLD.

In the present study, JUN interacted with BCL2L11 and was targeted by miR-20a in the miRNA-lncRNA-mRNA interaction network. The serum/plasma level of miR-20a has potential value for detecting hepatitis C virus (HCV) infection, and therefore circulating miR-20a may be useful as a predictive marker in liver fibrosis mediated by HCV (41). Apoptosis is a major cause of hepatocyte elimination in NAFLD, and inhibition of the anti-apoptotic protein Bcl-2 and activation of the pro-apoptotic protein p53 promotes inflammation in NAFLD (42). Overexpression of Bcl-2 results in resistance to reperfusion injury in the ischemic liver by suppressing apoptosis and is associated with increased caspase 3 and cytoplasmic cytochrome c and a deficiency of Bcl-extra large (43). By targeting the anti-apoptotic gene Bcl-2, miR-15b and miR-16 regulate tumor necrosis factor-mediated hepatic apoptosis in the process of acute liver failure (44). The data from the present study suggest that miR-20a serves a role in NAFLD by targeting BCL2L11 and affecting the expression of JUN.

JUN was also regulated by the miRNAs of miR-409-3p and miR-139. In hepatoma HuH-7 cells, miR-409-3p decreased the production of fibrinogen by downregulating fibrinogen beta chain precursor expression (45). A previous study suggested that miR-409-3p may be utilized to detect the progression of NAFLD (46). miR-409-3p was also identified as a biomarker for the therapeutics and diagnosis of a number of heart failure-associated diseases and a risk factor of NAFLD (47). Overexpression of miR-139, which was downregulated in hepatocellular carcinoma (HCC) samples, suppresses the progression and metastasis of HCC by downregulating rho-kinase 2, indicating that miR-139 may be used to predict the outcome of HCC (48). In the present study, miR-409-3p and miR-139 interacted with the lncRNAs of AC004540.4 and RP11-279F6.1, respectively. Furthermore, the expression levels of AC004540.4 and RP11-279F6.1 were markedly enhanced in liver tissues from patients with NAFLD compared with the control liver samples. Therefore, we hypothesized that miR-409-3p, miR-139, AC004540.4 and RP11-279F6.1 co-regulated JUN expression in patients with NAFLD.

Although the present study succeeded in identifying specific key miRNAs and lncRNAs in the development of NAFLD, there were also certain limitations. For example, the analyses were based on the microarray dataset GSE72756 downloaded from the GEO database. However, the stages of NAFLD (fatty liver, steatohepatitis or fibrosis/cirrhosis) were not clearly described in the GEO database. Therefore, the degree of NAFLD was not clear. In addition, the sample size was too small, and the predicted regulatory associations were not validated. Future studies will aim to confirm the predicted regulatory associations using cell line experiments.

In conclusion, 318 DE-lncRNAs and 609 DE-mRNAs were identified in NAFLD liver tissues by bioinformatics analysis. Additionally, specific mRNAs (including JUN and BCL2L11) and miRNAs (including miR-20a, miR-409-3p and miR-139) may serve essential roles in the pathogenesis of NAFLD. The lncRNAs AC004540.4 and RP11-279F6.1 were also implicated in the mechanisms of NAFLD.

Acknowledgements

Not applicable.

Glossary

Abbreviations

lncRNAs

long non-coding RNA

miRNAs

microRNA

DE-lncRNAs

differentially expressed lncRNAs

PPI

protein-protein interaction

NAFLD

non-alcoholic fatty liver disease

NASH

non-alcoholic steatohepatitis

FATP5

fatty acid transport protein 5

FDR

false discovery rate

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

CC

Closeness centrality

DC

Degree Centrality

BC

Betweenness centrality

RT-qPCR

reverse transcription quantitative polymerase chain reaction

BP

biological process

MF

molecular function

CC

cellular component

HCC

hepatocellular carcinoma

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

HW and XS were responsible for the conception and design of the research and drafting the manuscript. ZT and YL performed the data acquisition. JZ performed the data analysis and interpretation. YS participated in the design of the study and performed the statistical analysis. All authors have read and approved the manuscript.

Ethics approval and consent to participate

The present study was approved by the Ethics Committee of Nanjing First Hospital. Informed consent was obtained from all patients.

Patient consent for publication

Informed consent was obtained from all patients.

Competing interests

The authors declare that they have no competing interests.

References

  • 1.Shaker M, Tabbaa A, Albeldawi M, Alkhouri N. Liver transplantation for nonalcoholic fatty liver disease: New challenges and new opportunities. World J Gastroenterol. 2014;20:5320–5330. doi: 10.3748/wjg.v20.i18.5320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Rinella ME. Nonalcoholic fatty liver disease: A systematic review. JAMA. 2015;313:2263. doi: 10.1001/jama.2015.5370. [DOI] [PubMed] [Google Scholar]
  • 3.Tolman KG, Dalpiaz AS. Treatment of non-alcoholic fatty liver disease. Ther Clin Risk Manag. 2007;3:1153–1163. [PMC free article] [PubMed] [Google Scholar]
  • 4.Clark JM, Diehl AM. Nonalcoholic fatty liver disease: An underrecognized cause of cryptogenic cirrhosis. JAMA. 2003;289:3000–3004. doi: 10.1001/jama.289.22.3000. [DOI] [PubMed] [Google Scholar]
  • 5.Omagari K, Kadokawa Y, Masuda J, Egawa I, Sawa T, Hazama H, Ohba K, Isomoto H, Mizuta Y, Hayashida K, et al. Fatty liver in non-alcoholic non-overweight Japanese adults: Incidence and clinical characteristics. J Gastroenterol Hepatol. 2002;17:1098–1105. doi: 10.1046/j.1440-1746.2002.02846.x. [DOI] [PubMed] [Google Scholar]
  • 6.Shen L, Fan JG, Shao Y, Zeng MD, Wang JR, Luo GH, Li JQ, Chen SY. Prevalence of nonalcoholic fatty liver among administrative officers in Shanghai: An epidemiological survey. World J Gastroenterol. 2003;9:1106–1110. doi: 10.3748/wjg.v9.i5.1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Lazo M, Hernaez R, Bonekamp S, Kamel IR, Brancati FL, Guallar E, Clark JM. Non-alcoholic fatty liver disease and mortality among US adults: Prospective cohort study. BMJ. 2011;343:d6891. doi: 10.1136/bmj.d6891. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Doege H, Grimm D, Falcon A, Tsang B, Storm TA, Xu H, Ortegon AM, Kazantzis M, Kay MA, Stahl A. Silencing of hepatic fatty acid transporter protein 5 in vivo reverses diet-induced non-alcoholic fatty liver disease and improves hyperglycemia. J Biol Chem. 2008;283:22186–22192. doi: 10.1074/jbc.M803510200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hoekstra M, Li ZS, Kruijt JK, Van Eck M, Van Berkel TJ, Kuiper J. The expression level of non-alcoholic fatty liver disease-related gene PNPLA3 in hepatocytes is highly influenced by hepatic lipid status. J Hepatol. 2010;52:244–251. doi: 10.1016/j.jhep.2009.11.004. [DOI] [PubMed] [Google Scholar]
  • 10.Lin YC, Chang PF, Hu FC, Yang WS, Chang MH, Ni YH. A common variant in the PNPLA3 gene is a risk factor for non-alcoholic fatty liver disease in obese Taiwanese children. J Pediatr. 2010;158:740–744. doi: 10.1016/j.jpeds.2010.11.016. [DOI] [PubMed] [Google Scholar]
  • 11.Tang Y, Bian Z, Zhao L, Liu Y, Liang S, Wang Q, Han X, Peng Y, Chen X, Shen L, et al. Interleukin-17 exacerbates hepatic steatosis and inflammation in non-alcoholic fatty liver disease. Clin Exp Immunol. 2011;166:281–290. doi: 10.1111/j.1365-2249.2011.04471.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yamada H, Suzuki K, Ichino N, Ando Y, Sawada A, Osakabe K, Sugimoto K, Ohashi K, Teradaira R, Inoue T, et al. Associations between circulating microRNAs (miR-21, miR-34a, miR-122 and miR-451) and non-alcoholic fatty liver. Clin Chim Acta. 2013;424:99–103. doi: 10.1016/j.cca.2013.05.021. [DOI] [PubMed] [Google Scholar]
  • 13.Sun C, Huang F, Liu X, Xiao X, Yang M, Hu G, Liu H, Liao L. miR-21 regulates triglyceride and cholesterol metabolism in non-alcoholic fatty liver disease by targeting HMGCR. Int J Mol Med. 2015;35:847–853. doi: 10.3892/ijmm.2015.2076. [DOI] [PubMed] [Google Scholar]
  • 14.Rui EC, Ferreira DM, Afonso MB, Borralho PM, Machado MV, Cortez-Pinto H, Rodrigues CM. miR-34a/SIRT1/p53 is suppressed by ursodeoxycholic acid in the rat liver and activated by disease severity in human non-alcoholic fatty liver disease. J Hepatol. 2012;58:119–125. doi: 10.1016/j.jhep.2012.08.008. [DOI] [PubMed] [Google Scholar]
  • 15.Sun C, Liu X, Yi Z, Xiao X, Yang M, Hu G, Liu H, Liao L, Huang F. Genome-wide analysis of long noncoding RNA expression profiles in patients with non-alcoholic fatty liver disease. IUBMB Life. 2015;67:847–852. doi: 10.1002/iub.1442. [DOI] [PubMed] [Google Scholar]
  • 16.Smyth GK. Limma: Linear models for microarray data. In: Bioinformatics and Computational Biology Solutions Using R and Bioconductor. In: Gentleman R, Carey VJ, Huber W, Irizarry RA, Dudoit S, editors. Springer New York; New York, NY: 2005. pp. 397–420. [Google Scholar]
  • 17.Kolde R, Kolde MR. Package ‘pheatmap’. https://cran.r-project.org/web/packages/pheatmap/ [Oct 12;2015 ]; [Google Scholar]
  • 18.Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS. 2012;16:284–287. doi: 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Tweedie S, Ashburner M, Falls K, Leyland P, McQuilton P, Marygold S, Millburn G, Osumi-Sutherland D, Schroeder A, Seal R, et al. FlyBase: Enhancing drosophila gene ontology annotations. Nucleic Acids Res 37 (Database Issue) 2009:D555–D559. doi: 10.1093/nar/gkn788. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Altermann E, Klaenhammer TR. PathwayVoyager: Pathway mapping using the Kyoto encyclopedia of genes and genomes (KEGG) database. BMC Genomics. 2005;6:60. doi: 10.1186/1471-2164-6-60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43 (Database Issue) 2015:D447–D452. doi: 10.1093/nar/gku1003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins. Nat Methods. 2012;9:1069–1076. doi: 10.1038/nmeth.2212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Tang Y, Li M, Wang J, Pan Y, Wu FX. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of biological networks. Biosystems. 2015;127:67–72. doi: 10.1016/j.biosystems.2014.11.005. [DOI] [PubMed] [Google Scholar]
  • 24.Du Y, Gao C, Chen X, Hu Y, Sadiq R, Deng Y. A new closeness centrality measure via effective distance in complex networks. Chaos. 2015;25:033112. doi: 10.1063/1.4916215. [DOI] [PubMed] [Google Scholar]
  • 25.Opsahl T, Agneessens F, Skvoretz J. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks. 2010;32:245–251. doi: 10.1016/j.socnet.2010.03.006. [DOI] [Google Scholar]
  • 26.Cukierski WJ, Foran DJ. Using betweenness centrality to identify manifold Shortcuts. Proc IEEE Int Conf Data Min. 2008;2008:949–958. doi: 10.1109/ICDMW.2008.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet. 2006;2:e88. doi: 10.1371/journal.pgen.0020088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24:69–71. [PMC free article] [PubMed] [Google Scholar]
  • 29.Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): Update 2013. Nucleic Acids Res. 2013;41:W77–W83. doi: 10.1093/nar/gkt439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Res. 2002;12:996–1006. doi: 10.1101/gr.229102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kozomara A, Griffiths-Jones S. miRBase: Annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42 (Database Issue) 2014:D68–D73. doi: 10.1093/nar/gkt1181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.John B, Enright AJ, Aravin A, Tuschl T, Sander C, Marks DS. Correction: Human MicroRNA targets. PLoS Biol. 2005;3:e264. doi: 10.1371/journal.pbio.0030264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Krüger J, Rehmsmeier M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 2006;34:W451–W454. doi: 10.1093/nar/gkl243. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262. [DOI] [PubMed] [Google Scholar]
  • 35.Dorn C, Engelmann JC, Saugspier M, Koch A, Hartmann A, Müller M, Spang R, Bosserhoff A, Hellerbrand C. Increased expression of c-Jun in nonalcoholic fatty liver disease. Lab Invest. 2014;94:394–408. doi: 10.1038/labinvest.2014.3. [DOI] [PubMed] [Google Scholar]
  • 36.Singh R, Wang YJ, Xiang YQ, Tanaka KE, Gaarde WA, Czaja MJ. Differential effects of JNK1 and JNK2 inhibition on murine steatohepatitis and insulin resistance. Hepatology. 2009;49:87–96. doi: 10.1002/hep.22578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Karin M, Gallagher E. From JNK to pay dirt: Jun kinases, their biochemistry, physiology and clinical importance. IUBMB Life. 2005;57:283–295. doi: 10.1080/15216540500097111. [DOI] [PubMed] [Google Scholar]
  • 38.Min L, Ji Y, Bakiri L, Qiu Z, Cen J, Chen X, Chen L, Scheuch H, Zheng H, Qin L, et al. Liver cancer initiation is controlled by AP-1 through SIRT6-dependent inhibition of survivin. Nat Cell Biol. 2013;15:1203–1211. doi: 10.1038/ncb2726. [DOI] [PubMed] [Google Scholar]
  • 39.Machida K, Tsukamoto H, Liu JC, Han YP, Govindarajan S, Lai MM, Akira S, Ou JH. c-Jun mediates hepatitis C virus hepatocarcinogenesis through signal transducer and activator of transcription 3 and nitric oxide-dependent impairment of oxidative DNA repair. Hepatology. 2010;52:480–492. doi: 10.1002/hep.23697. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  • 40.Trierweiler C, Hockenjos B, Zatloukal K, Thimme R, Blum HE, Wagner EF, Hasselblatt P. The transcription factor c-JUN/AP-1 promotes HBV-related liver tumorigenesis in mice. Cell Death Differ. 2016;23:576–582. doi: 10.1038/cdd.2015.121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Shrivastava S, Petrone J, Steele R, Lauer GM, Di Bisceglie AM, Ray RB. Up-regulation of circulating miR-20a is correlated with hepatitis C virus-mediated liver disease progression. Hepatology. 2013;58:863–871. doi: 10.1002/hep.26296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Panasiuk A, Dzieciol J, Panasiuk B, Prokopowicz D. Expression of p53, Bax and Bcl-2 proteins in hepatocytes in non-alcoholic fatty liver disease. World J Gastroenterol. 2006;12:6198–6202. doi: 10.3748/wjg.v12.i38.6198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Selzner M, Rüdiger HA, Selzner N, Thomas DW, Sindram D, Clavien PA. Transgenic mice overexpressing human B cl-2 are resistant to hepatic ischemia and reperfusion. J Hepatol. 2002;36:218–225. doi: 10.1016/S0168-8278(01)00259-8. [DOI] [PubMed] [Google Scholar]
  • 44.An F, Gong B, Wang H, Yu D, Zhao G, Lin L, Tang W, Yu H, Bao S, Xie Q. miR-15b and miR-16 regulate TNF mediated hepatocyte apoptosis via BCL2 in acute liver failure. Apoptosis. 2012;17:702–716. doi: 10.1007/s10495-012-0704-7. [DOI] [PubMed] [Google Scholar]
  • 45.Fort A, Borel C, Migliavacca E, Antonarakis SE, Fish RJ, Neerman-Arbez M. Regulation of fibrinogen production by microRNAs. Blood. 2010;116:2608–2615. doi: 10.1182/blood-2010-02-268011. [DOI] [PubMed] [Google Scholar]
  • 46.Tryndyak VP, Marrone AK, Latendresse JR, Muskhelishvili L, Beland FA, Pogribny IP. MicroRNA changes, activation of progenitor cells and severity of liver injury in mice induced by choline and folate deficiency. J Nutr Biochem. 2016;28:83–90. doi: 10.1016/j.jnutbio.2015.10.001. [DOI] [PubMed] [Google Scholar]
  • 47.Yang Y, Yu T, Jiang S, Zhang Y, Li M, Tang N, Ponnusamy M, Wang JX, Li PF. miRNAs as potential therapeutic targets and diagnostic biomarkers for cardiovascular disease with a particular focus on WO2010091204. Expert Opin Ther Pat. 2017;27:1021–1029. doi: 10.1080/13543776.2017.1344217. [DOI] [PubMed] [Google Scholar]
  • 48.Wong CC, Wong CM, Tung EK, Au SL, Lee JM, Poon RT, Man K, Ng IO. The microRNA miR-139 suppresses metastasis and progression of hepatocellular carcinoma by down-regulating Rho-kinase 2. Gastroenterology. 2011;140:322–331. doi: 10.1053/j.gastro.2010.10.006. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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