Comprehensive bioinformatics analysis of critical lncRNAs, mRNAs and miRNAs in non‑alcoholic fatty liver disease
- Authors:
- Published online on: February 5, 2019 https://doi.org/10.3892/mmr.2019.9931
- Pages: 2649-2659
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Copyright: © Wu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
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.
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 II.Top 15 nodes with increased DC, BC and CC scores in the protein-protein interaction network. |
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. |
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. |
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).
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.
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.
Glossary
Abbreviations
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 |
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