Identify Functional lncRNAs in Nonalcoholic Fatty Liver Disease by Constructing a ceRNA Network.

Aim: To identify functional long noncoding RNAs (lncRNAs) by constructing a NAFLD-related lncRNA-miRNA-mRNA network (NLMMN) based on the hypothesis that lncRNAs, as competitive endogenous RNAs (ceRNAs), are able to regulate mRNA functions by competitive binding to shared miRNAs.
Methods: The "Limma R package" was used to identify differentially expressed lncRNAs and mRNAs (DElncRNAs and DEmRNAs). The "miRcode online tool" was used to predict the potential interactions between DElncRNAs or DEmRNAs using Perl, and "multiMiR R package" was used to predict the potential interactions between DElncRNAs and miRNAs. The NLMMN was viewed by Cytoscape. The DEmRNAs were further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to identify functional lncRNAs in human liver tissue and FFAs-induced fat-overloading HepG2 cells. The role of functional lncRNA was explored in the HepG2 cell line.
Results: A total of 336 DElncRNAs (154 upregulated and 182 downregulated, |log 2 (fold change) |>0.655 and P < 0.05) and 399 DEmRNAs (152 upregulated and 247 downregulated, |log 2 (fold change) |>0.608 and P < 0.05) were identified. A total of 142 DElncRNA-miRNA interaction pairs and 643 miRNA-DEmRNA interaction pairs were retained to construct the NLMMN, which contained 19 lncRNAs, 47 miRNAs, and 228 mRNAs. The results of GO and KEGG enrichment analyses were related to an extracellular matrix (ECM). Two upregulated lncRNAs (LINC00240 and RBMS3-AS3) and one downregulated lncRNA (ALG9-IT1) were identified by qRT-PCR in liver tissues. But only LINC00240 was significantly upregulated in fat-overloading HepG2 cells. Overexpression of LINC00240 did not affect lipid accumulation but increased the reactive oxygen species (ROS) content in HepG2 cells.
Conclusion: LINC00240, RBMS3-AS3, and ALG9-IT1 might be novel functional lncRNAs that attenuate liver fibrosis in NAFLD by influencing the ECM through the ceRNA network. Among them, LINC00240 might have a key role.

Introduction

Nonalcoholic fatty liver disease (NAFLD) does not refer to a certain disease but a spectrum of clinical and pathological severities, including simple steatosis, nonalcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and even hepatocellular carcinoma (HCC).[1,2] The previous “second-hit hypothesis” proposed that the accumulation of lipids in the cytoplasm of liver cells (the first hit) triggers a series of cytotoxic events (the second hit) that lead to liver inflammation. The hypothesis has been updated to the “parallel multiple hits hypothesis”,[3] which indicates that NAFLD should not be understood as a pathological “continuous spectrum” but a heterogeneous disease that takes different factors together to lead to liver inflammation and fibrosis by unknown pathways. In short, the pathogenesis of NAFLD is very complicated.[4] The current global prevalence of NAFLD is approximately 25.24%.[5] Over the past several decades, the prevalence of NAFLD has increased significantly worldwide.[2] A study showed that NASH is the most rapidly growing cause of HCC among patients who are listed for liver transplantation in the United States.[6] The clinical and economic burden of NAFLD has become enormous.[5,7] Therefore, we urgently need to explore emerging examination and treatment targets.

Over the years, protein-coding genes and their regulatory networks have been studied extensively. The development of transcriptomics and proteomics has revealed the regulatory role of noncoding RNAs (ncRNAs), a type of RNA that has no protein translation ability on gene expression and function.[8] Transcripts, mostly noncoding, cover 62–75% of our genome and contribute a lot to the overall estimate of 80% of the potential functional sequences in our DNA.[9] Long noncoding RNAs (lncRNAs), which are greater than 200 nucleotides in length, account for the majority of ncRNAs.[10,11] LncRNAs play important roles in the pathophysiology of many diseases.[11] An increasing number of studies have shown that lncRNAs are an emerging class of metabolic regulators.[12] Although many studies have explored the role of lncRNA in the development of NAFLD,[13] they remain largely unknown.

Recent studies have shown that lncRNAs, as competitive endogenous RNAs (ceRNAs) with microRNA (miRNA) response elements, can compete with mRNAs by competitive binding to shared miRNAs, thus affecting gene expression.[14] Considering the potential relevance of lncRNAs in the development of NAFLD through the ceRNA mechanism, in this study, we used bioinformatics tools to construct a NAFLD-related lncRNA–miRNA–mRNA network (NLMMN) to identify functional lncRNAs that might impact the development of NAFLD. The role of lncRNAs in NAFLD can be further explored by analyzing the NLMMN, which may bring new ideas for the diagnosis and treatment strategies of NAFLD. Consequently, it is necessary to investigate the role of the ceRNA network in NAFLD.

Material and Methods

Gene Expression Profile

LncRNA and mRNA expression profiles from the GSE107231 dataset were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). After acquiring the data, the Limma package in R 4.0.0 (the R Foundation for Statistical Computing, Vienna, Austria) was used to normalize the gene expression of each sample.

Probe Reannotation Pipeline

The probe sequences of the GPL20115 platform were downloaded from the GEO database (https://ftp.ncbi.nlm.nih.gov/geo/platforms/GPL20nnn/GPL20115/soft/). Then, the probe sequences were aligned with the GENCODE (https://www.gencodegenes.org, version 34) nucleic acid library, and probes that matched successfully were retained. At the same time, we used the average value to represent the expression of genes, and the duplicate probes were removed, leaving the probe with the largest average value. Finally, the expression profile data were divided into two parts: lncRNAs and mRNAs.

Differential Gene Expression Analysis of lncRNAs and mRNAs

Differential analysis was performed on lncRNAs and mRNAs in NAFLD vs. normal samples using the classic Bayes method provided by the Limma package. |log 2 (fold change) | > log 2 (fold change) cutoff and P < 0.05 were considered to be significant.Principal component analysis (PCA) was performed by the “FactoMineR package” and the “factoextra package.” The differentially expressed (DE) lncRNAs and mRNAs are shown as heat maps and volcano maps by the “pheatmap package” and the “ggplot2 package,” respectively.

Construction of the NLMMN

The miRNA target interactions needed to be identified. The miRcode (http://www.mircode.org/) online tool and Perl were used to predict the potential interactions between DElncRNAs and miRNAs. The “multiMiR package” was used to predict potential interactions between miRNAs and DEmRNAs. The shared miRNAs obtained by these two methods were identified as the predicted DEmiRNAs (pDEmiRNAs). The DElncRNA-miRNA and miRNA–DEmRNA pairs containing pDEmiRNAs were retained. Then, the final DE lncRNA–miRNA–mRNA regulatory relationship in the NLMMN was obtained. The NLMMN was viewed using Cytoscape (http://www.cytoscape.org/; version 3.7.2).

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analyses

The “ClusterProfiler package” was used for GO and KEGG enrichment analyses. GO was used to describe gene functions along three aspects: biological process (BP), cellular component (CC), and molecular function (MF). The significance level was set at a P < 0.05 (non or Benjamin Hochberg corrected).

Inclusion and Exclusion Criteria

The study was conducted in patients who presented to the First Affiliated Hospital of Chongqing Medical University between June 2016 and December 2021. Inclusion criteria were adults with hepatic pathology findings of ≥5% hepatic steatosis. The exclusion criteria were as follows: (a) pregnant women; (b) presence of infection with other viruses (HAV, HBV, HCV, HDV, HEV, and HIV); (c) presence of other liver diseases, such as Wilson’s disease; (d) a history of malignancy or hematological diseases; (e) patients who had undergone liver transplantation; (f) suffering from autoimmune diseases; (g) excessive alcohol consumption (>30 g/day in men, >20 g/day in women); (h) taking medication which affects liver status; (i) insufficient laboratory and clinical data. The present study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Informed consent was obtained from all patients.

Cell Culture Treatment

HepG2 cells, a human hepatoblastoma cell line, were culture in high glucose Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and kept at 37 °C in 5% CO2. Free fatty acids (FFAs) (oleic acid [OA, Sigma-Aldrich, O1383]: palmitic acid [PA, Sigma-Aldrich, P0500], 2:1) were dissolved in a solution of FFA-free bovine serum albumin (FAF-BSA, A8850, Solarbio). To induce fat-overloading of cells, HepG2 cells were exposed to the mixture of FFAs (OA/PA, 2:1) at the final concentration of 1 mM with 1% FAF-BSA 24 h after seeding.

Oil Red O (ORO) Staining and Determination of Intracellular Lipid Accumulation

HepG2 cells were treated with FFAs for 24 h. According to the manufacturer’s instructions, the ORO staining kit (G1262, Solarbio) was used for lipid staining, and the TG detection kit (BC0625, Solarbio) was used to detect intracellular TG content. Each group repeated three individual experiments.

Cell Transfection

HepG2 cells were seeded into 6-well plates so that the cells were 70% confluent at the time of transfection. To construct LINC00240 over-expression plasmids, human LINC00240 cDNA was synthesized and cloned into a pcDNA3.1 vector by GeneChem (Shanghai, China). An empty plasmid pcDNA3.1 served as the negative control (NC). Transfection was carried out using Lipofectamine 3000 (L3000008, Invitrogen; Thermo Fisher Scientific) according to the manufacturer’s instructions. Total RNA was collected 24 h after transfection.

Reactive Oxygen Species (ROS) Assay

Intracellular ROS generation was detected by the ROS detection kit (S0033, Beyotime) according to the manufacturer’s instructions. Then, a fluorescence microplate reader was used to measure excitation at 488 nm and emission at 525 nm.

Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (qRT–PCR)

Total RNA was isolated from patient’s liver tissues and HepG2 cells using RNAiso Plus (Takara, Tokyo, Japan). The purified RNA was used as a template to generate first-strand cDNA with a PrimeScript RT reagent Kit with gDNA Eraser (Takara, Tokyo, Japan). The primer sequences used for qRT–PCR are shown in Supporting Table S1.

Statistical Analysis

The relative RNA expression levels were calculated using the 2–ΔΔCt method. Images were analyzed by Image-Pro Plus 6.0. GraphPad Prism 8 software was used to analyze data with unpaired Student’s t-test for two groups. The F test was used to compare variances, and the unpaired t-test with Welch’s correction was used to analyze data when the variances were unequal. P < 0.05 was considered statistically significant.

Results

Data Collation

The gene expression data of the GEO database GSE107231, which were generated using the GPL20115 Agilent-067406 Human CBC lncRNA + mRNA microarray V4.0 (probe name version) platform, were downloaded. The sample set of GSE107231 included five NAFLD liver tissues (five males; mean age = 54.8 years) and five normal liver tissues (five males; mean age = 52.8 years). NAFLD was confirmed by pathological examination. We normalized the gene expression data using the Limma package of R software (Figure ).

Figure 1

Normalization of the dataset. (A) Before normalization and (B) after normalization.

Differential Gene Expression Analysis of lncRNAs and mRNAs

The expression profiles of the data were divided into two parts: lncRNAs and mRNAs, and PCA showed a clear separation between the NAFLD group and the normal group in both parts (Figure S1). A total of 399 differentially expressed mRNAs (DEmRNAs: 152 upregulated and 247 downregulated, |log 2 (fold change) |>0.608 and P < 0.05 [NAFLD/normal]) (Table S2) and 336 differentially expressed lncRNAs (DElncRNAs: 154 upregulated and 182 downregulated, |log 2 (fold change) |>0.655 and P < 0.05 [NAFLD/normal]) (Table S3) were identified. DERNAs were visualized in volcano and plot heatmaps (Figure ).

Figure 2

Differential gene expression. (A) Volcano plot of mRNAs, (B) volcano plot of lncRNAs, (C) heatmap of differentially expressed mRNAs, and (D) heatmap of differentially expressed lncRNAs.

A total of 571 DElncRNA-miRNA interaction pairs were downloaded from miRcode, and 47 372 miRNA–DEmRNA interaction pairs were identified by the “multiMiR packag”. After selecting the pairs containing the shared miRNAs, 142 DElncRNA-miRNA interaction pairs (Table S4) and 643 miRNA–DEmRNA (Table S5) interaction pairs were retained to construct the NLMMN (Figure ). This network contained 19 lncRNAs, 47 miRNAs, and 228 mRNAs.

Figure 3

Nonalcoholic fatty liver disease (NAFLD)-related lncRNA–miRNA–mRNA network (NLMMN). Red indicates lncRNAs, blue indicates mRNAs, and green indicates miRNAs.

GO and KEGG Enrichment Analyses of DEmRNAs

GO and KEGG enrichment analyses were performed on DEmRNAs (Figure ). The result of GO analysis of all 399 DEmRNAs indicated significant enrichment in six GO terms (Figure A and Table , P < 0.05, Benjamin and Hochberg-corrected), and the result of KEGG analysis showed that two pathways were found to be enriched (Figure B: P < 0.05). GO analysis results of 228 DEmRNAs in the NLMMN indicated significant enrichment in five GO terms (Figure C and Table : P < 0.05, Benjamin and Hochberg-corrected). Meanwhile, only one pathway (ECM-receptor interaction) was found to be enriched by KEGG enrichment analysis of 228 mRNAs in the NLMMN, which was downregulated. (Figure D: P < 0.05).

Figure 4

Functional enrichment of DEmRNAs, (A) circle diagram of the DEmRNAs specifically enriched by GO analysis; (B) circle diagram of DEmRNAs specifically enriched by KEGG analysis; (C) circle diagram of the DEmRNAs in the NLMMN specifically enriched by GO analysis; (D) circle diagram of the DEmRNAs in the NLMMN specifically enriched by KEGG analysis. The size of the bubble represents the number of enriched DEmRNAs, and the color represents the P value.

Table 1

Enriched GO Terms of the DEmRNAs

terms	pathway description	count	P-value	adjusted P-value
GO. BP:0015800	acidic amino acid transport	8	2.77 × 10–5	4.42 × 10–2
GO. BP:0051965	positive regulation of synapse assembly	8	3.46 × 10–5	4.42 × 10–2
GO. BP:0051048	negative regulation of secretion	15	3.77 × 10–5	4.42 × 10–2
GO. BP:0060453	regulation of gastric acid secretion	4	4.99 × 10–5	4.42 × 10–2
GO.CC:0062023	collagen-containing extracellular matrix	21	2.11 × 10–5	7.17 × 10–3
GO.MF:0005201	extracellular matrix structural constituent	13	1.31 × 10–5	7.02 × 10–3

Table 2

Enriched GO Terms of the DEmRNAs in the NLMMN

terms	pathway description	count	P-value	adjusted P-value
GO.CC:0062023	collagen-containing extracellular matrix	15	4.42 × 10–5	1.21 × 10–2
GO.CC:0005604	basement membrane	7	8.39 × 10–5	1.21 × 10–2
GO.CC:0008305	integrin complex	4	3.49 × 10–4	3.35 × 10–2
GO.CC:0098636	protein complex involved in cell adhesion	4	5.02× 10–4	3.61 × 10–2
GO.MF:0005201	extracellular matrix structural constituent	11	3.33× 10–6	1.46 × 10–3

DEmRNAs, differentially expressed mRNAs; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. NLMMN, nonalcoholic fatty liver disease (NAFLD)-related lncRNA–miRNA–mRNA network, ECM, extracellular matrix.

Information of Patients

A total of three normal liver tissues (one female and two males; age range: 52–57 years; mean age = 55.53 years) and three NAFLD liver tissues (two females and one male; age range: 59–66 years; mean age = 61.7 years) were provided by The First Affiliated Hospital of Chongqing Medical University (Chongqing, China) with pathological sections (Figure ).

Figure 5

Histopathological examination of liver tissue. (A–C) Normal liver tissues; (D–F) NAFLD liver tissues.

qRT–PCR of the lncRNAs in the NLMMN in Liver Tissues

qRT–PCR analysis was performed to determine the expression levels of these lncRNAs in the NLMMN. All identified candidate lncRNAs were assayed by qRT–PCR on a sample set of three NAFLD patients and three controls. The qRT–PCR results showed that two lncRNAs (LINC00240 and RBMS3-AS3) were upregulated, whereas one (ALG9-IT1) was downregulated in liver tissues of NAFLD patients (Figure and Table S6).

Figure 6

Levels of LINC00240, RBMS3-AS3, and ALG9-IT1 expression in normal liver tissues, and NAFLD liver tissues were detected using qRT–PCR. *P < 0.05, **P < 0.01, ***P < 0.001 by Student’s t-test or unpaired t-test with Welch’s test.

Upregulation of LINC00240 in HepG2 with Lipid Accumulation

Our previous results suggested that LINC00240 and RBMS3-AS3 were upregulated, whereas one ALG9-IT1 was downregulated in liver tissues of NAFLD patients. After treatment with FFA for 24 h in HepG2, oil red O staining showed that the intracellular lipid droplets increased (Figure A), and the intracellular TG content increased significantly at the same time (Figure B). These results suggest that the NAFLD cell model was successfully constructed. We redetected these three lncRNAs using qRT-PCR in HepG2 with lipid degeneration by qRT–PCR. The results suggested that LINC00240 was significantly upregulated in HepG2 with lipid accumulation (Figure C), while the difference of ALG9-IT1 was not statistically significant (Figure D). The expression abundance of RBMS3-AS3 was too low to detect.

Figure 7

(A) Oil Red O staining of HepG2 cells treated with FFA; (B) intracellular TG content of HepG2 cells treated with FFA; (C) the relative expression level of LINC00240 in FFA-treated and untreated HepG2 cells; (D) the relative expression level of ALG9-IT1 in FFA-treated and -untreated HepG2 cells; (E) detection of the transfection rate of LINC000240 in the overexpression group by qRT–PCR; (F) detection of ROS content in HepG2 cells overexpressing LINC00240; (G) Oil Red O Stain of HepG2 cells overexpressing LINC00240. RFU: relative fluorescence units; Control: FFA-untreated; Model: FFA-treated. Error bars represent the mean ± SEM, n = 3,*P < 0.05, **P < 0.01, ***P < 0.001.

Overexpression of LINC00240 Did Not Affect Lipid Accumulation but Increased ROS Content in HepG2 Cells

In order to explore the role of LINC00240 in NAFLD, we treated HepG2 cells with FFAs to mimic fatty overload conditions. HepG2 cells were transfected with pcDNA3.1 and pcDNA3.1-LINC00240. The results of qRT-PCR showed that transfection of pcDNA3.1-LINC00240 efficiently increased the expression of LINC00240 in HepG2 cells (Figure E). Interestingly, overexpression of LINC00240 increased the ROS content in hepG2 (Figure F and Table S7). However, overexpression of LINC00240 showed no effect on FFA-induced lipid accumulation in fat-overloading HepG2 cells (Figure G and Table S8).

Discussion

In recent years, NAFLD has become increasingly prevalent worldwide. However, there is still a lack of effective diagnostic methods and treatment options for NAFLD.[15] As ceRNAs, lncRNAs can participate in the regulation of gene expression. The imbalance of ceRNA and ceRNA networks may have an impact on NAFLD and can also help us understand the disease process and provide new ideas for new therapies.

This study identified key lncRNAs by constructing an NLMMN. Here, we identified 336 DElncRNAs and 399 DEmRNAs based on the GEO database GSE107231. Then, we built an NLMMN, which contains 19 lncRNAs, 47 miRNAs, and 228 mRNAs, and we performed GO and KEGG enrichment analyses. The results of GO enrichment analysis revealed that the mRNAs in the NLMMN were primarily enriched in the extracellular matrix (ECM) (collagen-containing extracellular matrix and extracellular matrix structural constituent). The results of KEGG enrichment analysis showed that the mRNAs in the NLMMN were primarily enriched during ECM-receptor interactions. The occurrence of fibrosis is related to the excessive production of ECM.[16,17] The results of GO and KEGG enrichment analyses suggest that lncRNAs mainly act on the ECM through the ceRNA network. Interestingly, most of these pathways are downregulated. Excessive accumulation of ECM can lead to fibrosis.[16,17] Therefore, we speculated that lncRNAs may increase the degradation of the ECM through the ceRNA network, thereby reducing the occurrence of liver fibrosis in NAFLD. NAFLD is associated with an increased risk of HCC development, but 20–30% of NAFLD-related HCC cases occur in the absence of cirrhosis.[18] Our study suggested that lncRNAs may play an important role in this phenomenon.

Several studies have revealed that multiple lncRNAs were involved in liver fibrosis, such as metastasis-associated lung adenocarcinoma transcript 1 (MALAT1),[19] transforming growth factor β 2-overlapping transcript 1 (TGFB2-OT1),[20] Alu-mediated p21 transcriptional regulator (APTR),[21] plasmacytoma variant translocation 1 (PVT1),[22] liver fibrosis-associated lncRNA1 (LFAR1),[23] and small nucleolar RNA host gene 7 (SNHG7).[24] And recently, some studies have shown that lncRNAs are involved in inflammation and fibrosis in the progression of liver steatosis. LncRNA nuclear enriched abundant transcript 1 (NEAT1) sponges miR-506 to regulate GLI3 expression to influence inflammation and fibrosis in NAFLD.[25] Zhang et al.[26] found that the expression of lncRNA maternally expressed gene 3 (MEG3) was upregulated in liver tissues of patients with liver fibrosis and cirrhosis caused by NASH. However, the role of lncRNAs in NAFLD fibrosis remains unclear. Our study reveals the importance of lncRNAs in NAFLD fibrosis from a macroscopic perspective for the first time.

In order to further explore the functional lncRNA, we used our patients’ liver tissue samples to verify the expression levels of all 19 lncRNAs in the NLMMN by qRT–PCR. The results showed that the expression levels of the two lncRNAs (LINC00240 and RBMS3-AS3) were significantly upregulated, and one lncRNA (ALG9-IT1) was significantly downregulated in the liver tissues from patients with NAFLD compared with the control liver samples. The trend of LINC00240 and ALG9-IT1 expression levels was consistent with the result of bioinformatics analysis, but the result of RBMS3-AS3 was opposite. The results of qRT-PCR showed that the entire expression of RBMS3-AS3 in liver tissue was very low, which may lead to poor stability of the detection data. This may lead to inconsistency between qRT-PCR results and microarray analysis results.

Then we further verified these three DElncRNAs in the HepG2 NAFLD model. The results show that only LINC00240 was significantly upregulated in HepG2 with lipid accumulation. Research by BU et al.[27] showed that LINC00240 could sponge miR-4465 to promote HCC cell proliferation, migration, and invasion. A meta-analysis of 231,355 individuals in a cohort of 82 Europeans revealed that seven single nucleotide polymorphisms (SPNs) were strongly associated with waist circumference (WC), one of which was located at LINC00240.[28] Increased WC was significantly associated with increased risk of NAFLD,[29] which suggested that LINC00240 might play certain roles in the occurrence and development of NAFLD. However, the biological role of LINC00240 in NAFLD is unclear. We explored the function of LINC00240 in NAFLD in the HepG2 NAFLD model. The increased production of ROS leads to molecular damage called oxidative stress, and NAFLD is influenced by a “multiple parallel-hit model” in which oxidative stress plays a central role.[30] Our results showed that overexpression of LINC00240 did not affect lipid accumulation but increased ROS content in HepG2 cells. This suggested that LINC00240 may have an effect on oxidative stress in hepatocytes. Hepatic stellate cells are key cells for ECM production.[31] And, LX2 is a widely used hepatic stellate cell line.[32] Our current study showed that LINC00240 is involved in apoptosis of LX2 (Wei Xue, Jia Zhang, Wenxiang Huang, unpublished data). This suggested that LINC00240 may be associated with liver fibrosis. And, our team will conduct further research on it.

Although this study successfully constructed an NLMMN, there are some limitations. First, the analyses were based on the microarray dataset GSE107231 downloaded from the GEO database, and the specific stages of NAFLD were not clearly described. In addition, the sample size was small because the liver tissues of NAFLD are very difficult to obtain compared to cancer tissues. Next, we will use laboratory methods to explore the functions of these lncRNAs. It is also hoped that more people will pay attention to the role of lncRNAs in the occurrence and development of NAFLD.

In conclusion, we constructed an NLMMN by analyzing an expression profile of NAFLD from dataset GSE107231 in the GEO database. The results of GO and KEGG enrichment analyses suggest that lncRNAs mainly act on the ECM through the ceRNA network to influence the development of NAFLD. We identified two lncRNAs (LINC00240 and RBMS3-AS3) that were upregulated and one lncRNA (ALG9-IT1) that was downregulated and may be involved in the development of NAFLD by qRT–PCR in patient’s liver tissues. But, only LINC00240 was significantly upregulated in HepG2 with lipid accumulation. So, LINC00240, RBMS3-AS3, and ALG9-IT1 might be the novel functional lncRNAs that attenuate liver fibrosis in NAFLD by influencing the ECM through the ceRNA network. Among them, LINC00240 might play a key role. The potential mechanisms of these lncRNAs in NAFLD should be researched to determine their feasibility as diagnostic or therapeutic biomarkers.