Serum miR-4488 as a potential biomarker of lean nonalcoholic fatty liver disease

Introduction

Nonalcoholic fatty liver disease (NAFLD) affects one-quarter of the adult population worldwide, contributing to a very large health burden (1). Based on epidemiological statistics, a dramatic increase in the percentage of the Chinese population affected by NAFLD (from 18% to 29%) occurred within one decade (2). A substantial proportion of patients with NAFLD are lean, as defined solely by body mass index (BMI) <23 kg/m², epitomizing the fallout of the rapid social transformation in Asia (3). Several studies have shown that dysregulation of lipid metabolism can severely affect NAFLD (4-6). A systematic review published in 2020 indicated that the global prevalence of lean NAFLD was 5.1% (7) and these individuals can develop all outcomes of NAFLD [i.e., cardiovascular disease (CVD) followed by extrahepatic malignancies and liver-related complications] and have long-term morbidity and mortality similar to those of their nonlean counterparts (8). However, due to the insidious onset of lean fatty liver disease, it is not easy to detect, and it is often in an advanced stage when diagnosed. Accordingly, it is urgent to explore potential biomarkers for noninvasive and early detection of lean NAFLD, preventing the misclassification of the lean subpopulation as having a benign disease.

To date, the underlying pathophysiology of lean NAFLD has not been thoroughly elucidated (9), and no approved drug therapy has been recommended (10). Genetics and epigenetics play a key role in the development of NAFLD (11). Genetic predisposition has been proposed as a pathogenic factor to better characterize this type of patient, and major candidate genes include PNPLA3, TM6SF2, CETP and PEMT (12). The most well-characterized common variant associated with lean NAFLD is the nonsynonymous variant of PNPLA3 corresponding to p.I148M (13), although Younes et al. indicated that NAFLD may progress to advanced liver disease independent of PNPLA3 genotype (14). These reports suggest that new candidate genes must be identified to more accurately recognize lean NAFLD. In addition, epigenetic changes interact with inherited risk factors to determine susceptibility to lean NAFLD (15). Gene expression regulation is provided by microRNAs (miRNAs), single-stranded noncoding RNAs composed of 20–24 nucleotides that function as regulators of gene expression and participate in the translation of proteins (16). Altered miRNA expression can thus influence the development and progression of a variety of pathophysiological processes (11). A study has shown that miR-122, the most highly expressed miRNA in the human liver, accelerates NAFLD progression, whereas miR-122 and miR-223 ameliorate it (17). A study by Liu et al. indicated that miR-192 expression leads to progression of NAFLD (18). These miRNAs have also been proposed as diagnostic biomarkers for liver injury and potential targets for treatment (15). However, few studies have shown the role of miRNAs in detecting lean NAFLD in the absence of obesity.

In this study, we isolated miRNAs from serum and assessed their expression levels in lean patients with NAFLD (LNs), nonlean patients with NAFLD (NLNs) and normal healthy individuals (HIs). The aim of this study was to explore the potential miRNA biomarkers that could explain the pathogenetic mechanisms of lean NAFLD. We present the following article in accordance with the STARD reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6620/rc).

Methods

Study design, patients and HIs

A total of 498 NAFLD patients and 98 HIs were enrolled between January 2020 and January 2022. The NAFLD patients consisted of two cohorts: one with BMI <23 kg/m² (LNs, n=98) and the other with BMI ≥23 kg/m² (NLNs, n=400). NAFLD requires: (I) evidence of hepatic steatosis either by imaging or histology, and (II) absence of other causes of hepatic fat accumulation from conditions such as significant alcohol consumption, hepatitis C, medication use, or hereditary disorders (19). Patients with significant alcohol consumption (>20 g/day for females and >30 g/day for males) or any preexisting liver disease were excluded. Healthy control subjects were selected from individuals seeking a routine health checkup at Longhua Hospital, Shanghai University of Traditional Chinese Medicine, and who had no evidence or history of liver disease, CVD or systemic disease that would result in their exclusion as a control in this study. Documented informed consent was given by each subject, and all aspects of the study were approved by the Ethics Committee of Longhua Hospital, Shanghai University of Traditional Chinese Medicine (No. 2020LCSY080). The study was performed in accordance with the relevant guidelines and regulations and the Declaration of Helsinki (as revised in 2013).

Blood samples and RNA isolation

Peripheral blood samples were centrifuged at 1,500 ×g for 10 min at 4 ℃. Serum was separated into EP tubes and stored at −80 ℃ until further use. The serum samples were divided into three comparison groups: 4 LNs vs. 4 HIs, 4 LNs vs. 6 NLNs, and 31 LNs vs. 62 NLNs vs. 72 HIs. The first two groups were used for next-generation sequencing analysis. A total of 165 samples (31 LNs vs. 62 NLNs vs. 72 HIs) were used to verify the selected candidate biomarker (Figure 1). Total RNA was extracted from serum using a TRIzol Kit (Invitrogen, Life Technologies, Carlsbad, CA, USA) and an RNeasy Serum Kit (Qiagen, Hilden, Germany) following the manufacturers’ instructions. The total RNA quantity and purity were confirmed with an Agilent High Sensitivity DNA Kit (Agilent Technologies, Inc., USA) using a Bioanalyzer 2100 (Agilent Technologies, Inc., USA).

Figure 1 Flow chart of the sequencing analysis. HI, healthy individual; LN, lean nonalcoholic fatty liver disease; NLN, nonlean nonalcoholic fatty liver disease; qRT-PCR, quantitative real-time polymerase chain reaction.

Small RNA library preparation and sequence analysis

Small RNA libraries were constructed using a New England Biolabs (NEB) NEBNext Multiplex Small RNA Library Prep Kit (MA, USA) for Illumina sequencers. The qualified libraries were sequenced on the Illumina HiSeq X Ten platform (Illumina, Shanghai, China). Sequencing reads were uploaded to the Repbase database. Adapter trimming, quality control and ambiguous read sorting were performed, and the reads were then annotated using miRBase.

Quantitative real-time polymerase chain reaction (qRT-PCR) analysis for identification and validation of candidate serum miRNAs

The candidate miRNAs were further validated by qRT-PCR performed using the QuantiFast^® SYBR^® Green PCR Master Mix in an ABI 9700 PCR System (Applied Biosystems, Foster City, CA, USA). The relative expression values of the target miRNAs were normalized to that of RNU6B (U6), and the differences in gene expression were analyzed using the 2^−∆∆Ct method. The primer sequence is shown in Table 1.

miRNA target gene prediction and pathway analysis

The TargetScan Human database (release 7.2, http://www.targetscan.org/vert_72/) and MicroRNA Target Prediction Database (miRDB; http://mirdb.org/) were used for the prediction of miRNA targets. Gene Ontology (GO; www.geneontology.org) enrichment analysis was then performed to analyze the main function of each putative target gene. Kyoto Encyclopedia Genes and Genomes (KEGG; www.genome.jp/kegg) enrichment analysis was applied to identify molecular pathways that were potentially altered.

Functional analysis of the validated or predicted targets of selected miRNAs was performed with STRING 10 (http://string-db.org/).

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics 26.0 (IBM Corp., Armonk, NY, USA), and graphs were generated with GraphPad Prism (v. 8.0.1, GraphPad Software, San Diego, CA, USA). Differential miRNA expression analysis was performed with DESeq2. Differential expression was assumed at a false detection rate <0.05. Comparisons among the three groups were performed using the nonparametric Kruskal-Wallis H test followed by the Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was performed for each validated miRNA to investigate its sensitivity and specificity in distinguishing group differences. The statistical significance of the area under the curve (AUC) was assessed with the Mann-Whitney U test. A P value <0.05 was considered statistically significant. The data are presented as the mean ± standard deviation (SD) values.

Results

Clinical characteristics of the subjects

The discovery cohort consisted of 98 LNs, 302 NLNs, and 98 HIs. Their clinical characteristics are summarized in Table 2. The LNs were older and had a smaller waist circumference, lower levels of alanine aminotransferase, glutamyl transpeptidase, fasting insulin, and uric acid, lower HOMA-IR score, and higher levels of total cholesterol, high-density lipoprotein cholesterol, and hemoglobin (P<0.05). No statistically significant differences were observed between the LN and NLN groups with regard to systolic and diastolic blood pressures, aspartate aminotransferase, total bilirubin, triglycerides, low-density lipoprotein cholesterol, fasting plasma glucose, HbA1c glycated hemoglobin, and C-reactive protein (P>0.05).

Altered miRNA expression patterns

The heatmap of all the samples showed differentially expressed miRNAs among the three groups (Figure 2A). In total, 8 miRNAs showed altered expression among 4 LNs, 6 NLNs and 4 HIs. When pairwise comparisons of the overall miRNA expression profile were analyzed with a heatmap, 2 miRNAs (miR-4488 and miR-5189-5p) showed altered expression between 4 LNs and 4 HIs, and both were upregulated (Figure 2B). In addition, 4 miRNAs showed altered expression between 4 LNs and 6 NLNs: 2 upregulated miRNAs (miR-4488 and miR-4443) and 2 downregulated miRNAs (miR-1255a and miR-4999-5p) (Figure 2C). A volcano plot revealed the differentially expressed miRNAs between 4 LNs vs. 4 HIs (Figure 2D) and 4 LNs vs. 6 NLNs (Figure 2E).

Figure 2 Results of next-generation sequencing analysis. (A) Heatmap based on Spearman rank correlation as the distance matrix indicating the microRNA (miRNA) expression profiles across the groups. When heatmaps (B,C) were plotted for pairwise group comparisons, the intensity of differentially expressed miRNAs was determined between the (B) LN and HI groups and the (C) LN and NLN groups. In each panel, the top axis shows the group of samples; the left axis shows the clustering of miRNAs; and the bottom axis shows the individuals. Volcano plot showing the differentially expressed miRNAs between 4 LNs vs. 4 HIs (D) and 4 LNs vs. 6 NLNs (E). NLN, nonlean nonalcoholic fatty liver disease; LN, lean nonalcoholic fatty liver disease; HI, healthy individual.

Selection and verification of a serum miRNA as a diagnostic biomarker

Subsequent analysis indicated that miR-4488 showed the same alteration patterns between LNs and HIs, and between LNs and NLNs; thus, it was selected as the candidate biomarker for lean NAFLD (Figure 3A). The serum level of miR-4488 (Figure 3B) was significantly higher in LNs than in HIs (P<0.0001). ROC curves were constructed to evaluate the diagnostic value of miR-4488 for LN, and the AUC was 0.794 [95% confidence interval (CI): 0.702–0.887] (Figure 3C). In addition, the serum level of miR-4488 was higher in LNs than in NLNs (P=0.025). ROC curves were constructed to evaluate the diagnostic value of miR-4488 for LN, and the AUC was 0.698 (95% CI: 0.583–0.812) (Figure 3D). ROC curve analysis was performed to evaluate whether the serum level of miR-4488 could be used as a potential diagnostic biomarker for lean NAFLD.

Figure 3 Serum level of microRNA, miR-4488, in the validation cohort (31 LNs vs. 62 NLNs vs. 72 HIs) by qRT-PCR. Venn diagram showing that miR-4488 had the same alteration patterns between LNs and HIs and between LNs and NLNs (A). The expression of miR-4488 was significantly higher in LNs than in HIs (P<0.0001) and NLNs (P=0.025) (B). ROC curve analysis showed that miR-4488 had diagnostic value in the LN vs. HI comparison; the AUC was 0.794 (95% CI: 0.702–0.887) (C). ROC curve analysis showed miR-4488 had diagnostic value in the LN vs. NLN comparison; the AUC was 0.698 (95% CI: 0.583–0.812) (D). ***, P<0.001. ns, not significant; HI, healthy individual; LN, lean nonalcoholic fatty liver disease; NLN, nonlean nonalcoholic fatty liver disease; qRT-PCR, quantitative real-time polymerase chain reaction; ROC, receiver operating characteristic; AUC, area under the curve; CI, confidence interval.

GO and KEGG enrichment analyses and construction of the PPI network for the key target genes of miR-4488

Based on the results of RNA sequencing and subsequent validation, the TargetScan and miRDB websites were used to predict the potential target genes of miR-4488. To further understand the functions of the predicted target genes of miR-4488, the main biological processes (BPs), cellular components (CCs) and molecular functions (MFs) identified by GO enrichment analysis of the target genes of miR-4488 were summarized (Figure 4A). Regulation of signaling and regulation of cell communication were the two most enriched BP subcategories; intrinsic component of plasma membrane and integral component of plasma membrane were the two most enriched CC subcategories; and GTPase binding was the most enriched MF subcategory.

Figure 4 Bioinformatic analysis of the predicted target genes of microRNA, miR-4488. GO enrichment analysis of the predicted target genes of miR-4488 (A). KEGG enrichment analysis of the predicted target genes of miR-4488 revealed the top 20 most enriched KEGG pathways (B). Protein-protein interaction network of the predicted target genes of miR-4488. The nodes are proteins that were predicted as the targets of miR-4488, and the edges represent the functional associations (C). CC, cellular component; MF, molecular function; BP, biological process; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.

To better understand the biological functions of the predicted target genes, KEGG enrichment analysis was performed to reveal the main pathways in which the candidate target genes may be involved. The top 20 statistically significantly enriched pathways were identified and are shown in Figure 4B. Choline metabolism in cancer (hsa05231), the tumor-necrosis factor (TNF) signaling pathway (hsa04668), p53 signaling pathway (hsa04115), circadian entrainment (hsa04713) and basal cell carcinoma (hsa05217) were the top five enriched pathways (Figure 4B).

Protein-protein interaction (PPI) network analysis is a viable tool to understand MFs and disease mechanisms. A PPI network with 30 nodes was constructed (Figure 4C), and the most important modules were then screened. Among the genes in these modules, ARHGAP1, SLC10A1, SIX5, WTIP, CTNNA1, BCL7A, MYOD1, GLB1L2, ASPA, and SLC9A3R2 were the 10 genes with the closest connections to other nodes.

Discussion

Emerging data suggest that NAFLD is present in a considerable proportion of lean individuals, as described by the previous study (20). Lean NAFLD used to be considered a benign disease, and clinicians always ignored its therapy and management (21). Considering that lean NAFLD patients can develop the full spectrum of liver damage that characterizes nonlean NAFLD (22), finding more accurate biomarkers for noninvasive and early detection of lean NAFLD is essential. Many efforts have been made to explain the progression of NAFLD according to genes and miRNAs (23). However, the pathophysiological mechanisms underlying NAFLD development in lean subjects are not entirely understood (24). Our data showed that serum miR-4488 may have potential for noninvasive and early detection of lean NAFLD. The serum miR-4488 level was significantly higher in LNs than in NLNs and HIs.

Very recently, the range of miRNA applications has broadened as they are increasingly used in different clinical settings for early disease detection and monitoring of disease progression. A study by Fang et al. (25) indicated that a reduction in miR-4488 expression induces venous endothelia cell inflammation via the COX-2/NFκB pathway, showing high potential for preventing venous graft disease. miR-4488 participates in autophagy by targeting the N-acetyl transferase 8-like (NAT8L) protein and affects mitochondrial function (26). miR-4488 is highly downregulated in adeno-associated virus transduction associated with hepatocellular carcinoma (27). Huang et al. (28) found a new miRNA signaling pathway in vascular endothelial cell autophagy and inflammation, showing that NAT8L was downregulated as an important target of miR-4488. However, the function of miR-4488 in lean NAFLD is unclear.

To predict the function of miR-4488 in lean NAFLD, we performed GO and KEGG enrichment analyses. Several enriched pathways were identified, among which choline metabolism in cancer, the TNF signaling pathway, p53 signaling pathway, circadian entrainment, and basal cell carcinoma were the top five. Activated choline metabolism, the TNF signaling pathway, and basal cell carcinoma are associated with hallmarks of carcinogenesis and tumor progression (29). Another pathway related to hepatocyte apoptosis identified in our study was the p53 signaling pathway (30). A previous study demonstrated that p53/DRAM-mediated mitophagy is a primary inducer of apoptosis in mild hepatosteatosis, whereas p53-induced BAX expression induces apoptosis mainly in severe hepatosteatosis (31). Circadian entrainment was another related pathway identified by the present study. A recent study revealed that morning circadian misalignment was associated with metabolic dysregulation in girls who were obese (32). Schwerbel et al. revealed that members of the immunity-related GTPase family act as regulators of hepatic fat accumulation, with links to autophagy (33). Overexpression of the Ifgga2 gene was shown to reduce hepatic lipid storage and could be a therapeutic target for fatty liver disease (33). Thus, miR-4488 may influence the progression of lean NAFLD by participating in these signaling pathways.

Moreover, our PPI network analysis revealed that miR-4488 regulatory targets relevant to lean NAFLD included ARHGAP1, SLC10A1, SIX5, CTNNA1, and WTIP. There are limited reports on these genes: studies on ARHGAP1 and the PI3K/AKT pathway in breast cancer (34), SLC10A1 and metabolic changes in hepatoblastoma cells (35), DMAHP/SIX5 in myotonic dystrophy (36), CTNNA1 in gastric and breast cancer (37), and WTIP and the Hippo pathway in hepatocellular carcinoma (38). The precise mechanisms still need to be fully described. However, the precise mechanism by which miR-4488 acts by regulating these targets in NAFLD still needs to be validated by more experiments.

To the best of our knowledge, ours is the first study to examine the correlation between the serum levels of miRNAs and lean NAFLD. However, significant correlations remain to be found between miR-4488 and its target genes. Further studies are needed in the future to clearly explain the pathophysiology and provide novel options for therapy.

Future translational research in this field may provide new diagnostic and therapeutic approaches to treat lean NAFLD, considering the miRNA-driven regulation of this disease. However, several limitations still exist. First, the diagnosis of sarcopenic obesity based solely on BMI is controversial. Waist circumference or body composition analysis should be used in future studies to better define lean NAFLD. Second, the cohort in the present study was relatively small, so the results still need to be verified in a larger population with other specific phenotypes.

Collectively, the integration of a molecular diagnosis in the clinical evaluation of patients with lean NAFLD will provide an accurate diagnosis with possible targeted therapies and may uncover novel molecular mechanisms with potential broader therapeutic implications.

Conclusions

In summary, this study showed that the serum level of miR-4488 was increased in LNs compared with HIs and NLNs. miR-4488 may be a potential biomarker for diagnosing and predicting the pathogenetic mechanisms of lean NAFLD.

Acknowledgments

Funding: This work was supported by the Scientific Research Project of Shanghai Science and Technology Commission (Grant No. 19zr1458100), the Science and Technology Innovation Action Plan of Shanghai Science and Technology Commission (No. 20y21902400), the Joint Research Project of Emerging Frontier Technology (No. shdc12019118), Longhua Innovative Project (No. CX202037), Longhua Medical Scholar (No. LYTD-77), the Training Plan of Famous Traditional Chinese Medicine Doctors in Pudong New Area of Shanghai (No. PWRzm2020-03), the Shanghai University of Chinese Medicine reserve outstanding TCM talents (No. 2020012), and Shanghai Municipal Health Commission and Shanghai Medical and Health Development Foundation, General Practice Project of New Star Young Medical Talents in Medical Sciences (No. 2020087).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-22-6620/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-6620/dss

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-22-6620/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-6620/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Documented informed consent was given by each subject, and all aspects of the study were approved by the Ethics Committee of Longhua Hospital, Shanghai University of Traditional Chinese Medicine (No. 2020LCSY080). The study was performed in accordance with the relevant guidelines and regulations and the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: K. Brown)