Introduction

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a metabolic stress-related liver injury characterized by excessive accumulation of fat in the liver. The global prevalence of MASLD in the general adult population ranges from 6.3 to 45%, with a median of 25.2%1. The prevalence of MASLD in China is at a moderate to high level, exceeding 25%2. The spectrum of MASLD includes metabolic dysfunction-associated steatosis, metabolic dysfunction-associated steatohepatitis (MASH), cirrhosis and hepatocellular carcinoma (HCC)3. The liver-related mortality rates for MASLD patients and MASH patients are 0.77 per 1000 person-years and 11.77 per 1000 person-years, respectively, and the all-cause mortality rates are 15.44 per 1000 person-years and 25.56 per 1000 person-years, respectively4.

The etiology and pathogenesis of MASLD are multifactorial and not yet fully understood. Insulin resistance (IR), lipid metabolism disorders, inflammation, the environment and imbalances in the gut microbiota imbalance may all contribute to the occurrence and development of MASLD5. Recent studies have highlighted the strong association between MASLD and inflammatory burden6,7. IR, obesity, and other metabolic abnormalities can lead to a state of chronic low-grade inflammation, which is a key factor in the pathogenesis of MASLD8. Moreover, IR leads to adipose tissue dysfunction via the secretion of adipokines and pro-inflammatory cytokines, thereby potentiating inflammation. At the cellular level, these processes result in oxidative stress and DNA damage in hepatocytes, which, combined with inflammation, are thought to eventually lead to fibrosis and cirrhosis9. Hence inflammation related pathways could be involved in the pathogenesis of MASLD.

At present, the treatment methods for MASLD include lifestyle interventions, drug therapy and complication management10. Effective treatments for MASLD remain challenging because the understanding of its pathogenesis is incomplete.

Over the past few decades, methodologies in systems biology have significantly advanced our comprehension of the molecular mechanisms underlying biological phenomena and pathologies11. These innovative strategies have played crucial roles in deciphering complex molecular interactions and associations across a spectrum of diseases. In order to identify new diagnostic and therapeutic targets for MASLD, we used bioinformatics approaches to explore potentially relevant biomarkers and the pathogenesis of MASLD in the present study.

Methods

Data acquisition and preliminary processing

Three MASLD-related datasets (GSE89632 series deposited by Allard JP et al., GSE72756 series deposited by Chuanzheng S et al. and GSE49541 series deposited by Caba O et al.) were downloaded from the Gene Expression Omnibus (GEO) database in NCBI (http://www.ncbi.nlm.nih.gov/gds/). The details of the datasets are presented in Table 1. GSE89632, GSE72756 and GSE49541 were merged to create a comprehensive dataset. The combined dataset contains 101 MASLD samples and 44 healthy control (HC) samples. Normalization of the data was conducted for batch effect correction via the ComBat function from the R package sva12. Further normalization was then applied via the normalizeBetweenArrays function from the R package limma13. The homogeneity of the data distribution after normalization was assessed by constructing box plots.

Table 1 Specifications of GSEs acquired from GEO.
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Identifcation of differential expressed genes (DEGs) associated with MASLD

The limma package in R was used to identify DEGs between the MASLD samples and HC group samples in the combined dataset. |logFC|> 1 and adjusted p value < 0.05 were set as the criteria for selection. A heatmap was created to represent the results visually.

Functional enrichment analysis of DEGs

As a standard method for functional enrichment studies, Gene Ontology (GO) analysis14 includes biological process (BP), molecular function (MF) and cellular component (CC) analyses. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were performed via the R package “clusterProfiler” in our study. The p value < 0.05 was considered to indicate significant enrichment.

Gene set enrichment analysis (GSEA) is a method that differs from traditional enrichment analyses as it is used to identify whether certain biological pathways are significantly affected within a gene set. Unlike methods that focus solely on the statistical significance of differential expression, GSEA takes into account the overall expression trend and consistency across a set of genes rather than just the individual significance of each gene. This approach allows GSEA to detect coordinated changes in gene expression that may not be apparent through traditional differential expression analysis, thus providing a more comprehensive view of the biological processes that are altered under specific conditions. The ClusterProfiler package of R software was used for GSEA of DEGs to explore the potential mechanism of MASLD preliminarily. p < 0.05 and FDR (q value) < 0.25 were considered the critical values for significant enrichment.

Network analysis of protein–protein interactions (PPIs) of DEGs and identification of MASLD-related hub genes

The STRING database (http://string-db.org/) was used to generate a PPI network for DEGs to investigate PPIs. The species was limited to “Homo sapiens”, and a medium confidence score > 0.9 was considered statistically significant. The PPI network relationship diagram was subsequently imported into the Cytoscape 3.9.1 platform and MCODE (Molecular Complex Detection) plugin was used to identify significant subnetworks within the PPI network. “In Whole Network” was chosen to identify clusters across the entire PPI network. The Degree Cutoff was set to 2 to filter out isolated nodes. The K-core was set to 2 to focus on subnetworks with a minimum level of connectivity. For cluster finding, the Node Score Cutoff was set to 0.2, Haircut was enabled to trim peripheral nodes, Fluff was disabled to maintain strict cluster boundaries, and the Max Depth from Seed was set to 100 to ensure a comprehensive search. The Cytohuba plugin’s Matthews correlation coefficient (MCC) algorithm was subsequently used to screen the top 10 genes on the basis of their importance in the PPI network as hub genes.

Results

Identification of DEGs related to MASLD

In total, 1278 DEGs related to MASLD were identified, with 1238 genes exhibiting a substantial increase in expression and 40 genes showing a marked decrease in expression in MASLD samples compared with healthy control samples. A heatmap of the DEGs in the dataset is plotted in Fig. 1.

Fig. 1
figure 1

Heatmap of DEGs associated with MASLD. HC, healthy controls.

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Results of enrichment analysis

As shown in Fig. 2, in the GO category, most of the DEGs were involved in the regulation of membrane potential, embryonic organ development, the ovulation cycle, embryonic organ morphogenesis and the regulation of postsynaptic membrane potential (BP); monoatomic ion channel complex, transporter complex, transmembrane transporter complex, synaptic membrane and interphotoreceptor matrix (CC); and postsynaptic neurotransmitter receptor activity, neurotransmitter receptor activity, neurotransmitter receptor activity involved in the regulation of postsynaptic membrane potential, gated channel activity and transmitter − gated monoatomic ion channel activity involved in the regulation of postsynaptic membrane potential (MF).

Fig. 2
figure 2

GO enrichment analysis. BP, biological process. MF, molecular function. CC, cellular component.

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The KEGG enrichment results indicated that the DEGs were involved in neuroactive ligand − receptor interaction, taste transduction, cytokine − cytokine receptor interaction, MAPK signaling pathway, autoimmune thyroid disease, the cAMP signaling pathway and the JAK − STAT signaling pathway (Fig. 3).

Fig. 3
figure 3

KEGG enrichment analysis. KEGG pathway analysis was performed using data from the KEGG database(Kanehisa Laboratories; https://www.kegg.jp)15,16.

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The GSEA results revealed that the neuroactive ligand–receptor interaction and taste transduction pathways were significantly downregulated in the MASLD group compared with those in the normal control group (Fig. 4).

Fig. 4
figure 4

GSEA results.

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Construction of the PPI network and identification of hub genes

A total of 1278 DEGs were input into the STRING database for PPI network analysis. After removing DEGs with different expression tendencies and isolated nodes from the PPI network, a total of 1331 nodes and 1471 edges were included, with an average node degree of 2.21 (Fig. 5). The top 10 genes, as identified by the MCC algorithm via the cytoHubba plugin in Cytoscape, were designated as hub genes, which included PIK3CD, PIK3R2, PIK3R1, PIK3R3, PIK3CB, PIK3CA, SRC, PIK3CG, PIK3R5 and PIK3R6 (Fig. 6).

Fig. 5
figure 5

PPI network of DEGs.

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Fig. 6
figure 6

The top 10 genes identified via the MCC algorithm served as hub genes.

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Discussion

As a significant global public health burden, MASLD is highly prevalent on all continents. MASLD, particularly its severe form known as MASH, is capable of advancing to severe liver conditions such as cirrhosis and HCC. MASH is increasingly recognized as the second leading cause of liver transplants in the United States, following chronic hepatitis C, and its prevalence continues to rise17. However, the underlying mechanisms of MASLD are still unclear. Investigating the pathogenesis of MASLD and finding effective treatments for MASLD are highly important. Many literatures have confirmed that MASLD, as well as related conditions like metabolic syndrome, obesity, type 2 diabetes mellitus, and hypertension, are all associated with significant inflammatory changes18,19,20. Liu YJ et al. reported that ACMSD inhibitors could mitigate inflammation and DNA damage, thereby reversing MASLD/MASH21. This study aims to provide some clues to support the pathogenesis and pharmacological studies of MASLD.

In our study, compared with healthy control individuals, 1278 DEGs related to MASLD were identified (1238 upregulated and 40 downregulated DEGs). According to the GO category, the DEGs were mostly involved in the regulation of membrane potential (BP), monoatomic ion channel complex (CC) and postsynaptic neurotransmitter receptor activity (MF). The liver is one of the most abundant organs in mitochondria. Growing evidence indicates that mitochondrial metabolism plays an important role in the development of MASLD in various ways. Mitochondrial dysfunction impairs the import and oxidation of fatty acids in mitochondria, leading to lipid accumulation and hepatic steatosis, which represents the initial stage of MASLD22. In MASLD, increased fatty acid oxidation and mitochondrial dysfunction lead to excessive ROS production, which damages mitochondrial components and creates a vicious cycle of oxidative stress, contributing to hepatocyte injury, inflammation, and fibrosis23. The mitochondrial membrane potential is an important indicator of mitochondrial function, reflecting the respiratory function and energy metabolism status of mitochondria. A decrease in the mitochondrial membrane potential is closely related to the occurrence and development of MASLD24,25. Monoatomic ion channel complex refers to a protein complex that spans across a membrane and phospholipid bilayer, allowing for the selective transport of monatomic ions along its electrochemical gradient. Previous studies have indicated that Ca2+ channel expression varies led to an imbalance in intracellular calcium levels, increased endoplasmic reticulum stress, impaired mitochondrial function and reduced autophagy, all of which contributed to the progression of MASLD26. The correlation between postsynaptic neurotransmitter receptor activity and MASLD is currently unclear. Yang Z et al. reported that neurotransmitters of the sympathetic nervous system (such as catecholamines and neuropeptide Y) could affect liver metabolism and the inflammatory response by activating specific receptors in the liver (such as adrenergic receptors and Y1 receptors), which suggested that postsynaptic receptor activity may indirectly affect the development of MASLD by affecting sympathetic nervous system activity in the liver27.

Our KEGG enrichment results indicated that the DEGs were involved mainly in neuroactive ligand–receptor interactions and taste transduction. GSEA further revealed that, compared with those in the normal control group, neuroactive ligand–receptor interactions and taste transduction pathways were significantly downregulated in the MASLD group. Wang A et al. confirmed that neuroactive ligand–receptor interactions played an important role in the common pathological mechanism of heart failure with a preserved ejection fraction and MASLD28. A longitudinal cohort study involving 33,899 Korean participants revealed that decreased overall autonomic modulation and vagal activity increased the risk of MASLD29. Sun W et al. reported consistent findings in the Chinese population30. Neuroactive ligand–receptor interactions encompasses a variety of neuroactive ligands and receptors that are involved in modulating metabolic and inflammatory processes, both of which are central to the development of MASLD. Neurotransmitters such as glutamate and GABA have been shown to influence insulin sensitivity and lipid metabolism through their respective receptors31. Additionally, as neuroactive ligands, leptin and adiponectin can regulate appetite and energy metabolism through receptors in the hypothalamus. Leptin can activate the JAK2 signaling pathway through its receptor Ob Rb, thereby activating signaling pathways such as STAT3, STAT5, MAPK, or AKT/mTOR, leading to IR. Adiponectin improves IR by activating the AMPK and PI3K/Akt signaling pathways through its receptors AdipoR1 and AdipoR232,33.

Taste transduction is traditionally associated with taste perception in the oral cavity, but emerging evidence suggests that it may also play a role in metabolic disorders and liver diseases, such as MASLD. Sarnelli G et al. confirmed that taste receptors such as TAS1R and TAS2R were expressed not only in the oral cavity but also in the gastrointestinal tract, where they modulated the release of enterohormones like glucagon-like peptide-1 (GLP-1) and peptide YY (PYY). These hormones play crucial roles in regulating glucose homeostasis, satiety, and energy balance, all of which are disrupted in MASLD34,35. It is reported that taste receptors in the gut could influence gut microbiota composition and function, thereby exacerbating inflammation and contribute to liver disease progression36.

We further identified 10 hub genes closely related to the occurrence of MASLD in the PPI network, including PIK3CD, PIK3R2, PIK3R1, PIK3R3, PIK3CB, PIK3CA, SRC, PIK3CG, PIK3R5 and PIK3R6. The PIK3 family is a class of phosphatidylinositol 3-kinases (PI3Ks) that play important roles in various physiological processes within cells, including cell proliferation, survival, metabolism and migration37. The PIK3 family can be divided into three different categories on the basis of their structure and function: type I, type II and type III. Type I PI3Ks, consisting of a catalytic subunit and a regulatory subunit, are the most extensively studied. The catalytic subunit includes four subtypes, α, β, γ and δ, which are encoded by the PIK3CA, PIK3CB, PIK3CG and PIK3CD genes, respectively. The regulatory subunits include PIK3R1, PIK3R2 and PIK3R3, which encode p85α, p85β and p55γ, respectively. The PIK3 family participates in the regulation of various cellular functions, such as cell survival, growth, metabolism and blood glucose homeostasis, through the PI3K/AKT signaling pathway38,39. The PI3K/AKT signaling pathway plays an important role in multiple mechanisms related to the presence and development of MASLD, such as lipid metabolism, the inflammatory response and IR. Previous studies have shown that activation of the PI3K/AKT signaling pathway can reduce intracellular lipid accumulation and inflammation levels, thereby alleviating MASLD. Vascular endothelial growth factor B (VEGFB) improves IR in MASLD through the PI3K/AKT signaling pathway. Wu D et al. reported that impaired PI3K/AKT signaling exacerbated IR in tissues, which lead to accelerated development of MASLD40. It has been reported that the overexpression of PIK3R3 promoted hepatic fatty acid oxidation by inducing PPAR α expression through PIK3R3, thereby improving the fatty liver phenotype induced by a high-fat diet in mice41. As a key regulatory subunit of the PI3K/AKT signaling pathway, downregulation of PIK3R3 expression may impair the full activation of this pathway, thereby contributing to IR, promoting hepatic lipid deposition, and consequently precipitating and exacerbating MASLD. It is reported that hesperetin mitigates oxidative stress in the liver through the PI3K/AKT-Nrf2 signaling pathway, and this antioxidant action further suppresses inflammation driven by NF-κB during the development of MASLD42. Liu B et al. demonstrated that scoparone improved hepatic inflammation in mice with MASLD by regulating the PI3K/AKT/mTOR pathway in macrophages43. SRC is a nonreceptor tyrosine kinase associated with multiple signaling pathways. We speculate that SRC may affect the occurrence and development of MASLD via the PI3K/AKT signaling pathway. We can develop inhibitors or activators targeting the hub genes to modulate the activity of the PI3K/AKT pathway for the treatment of MASLD. Sun C et al. reported that Pueraria flavonoids induced autophagy through the PI3K/AKT/mTOR signaling pathway, improved MASLD in obese mice and reduced intracellular lipid deposition by inhibiting lipid synthesis and the release of proinflammatory cytokines44. Wu YC et al. also reported that nucleoporin 85 mitigated lipid metabolism and inflammation by modulating the PI3K/AKT signaling pathway in MASLD45. Several drugs targeting the PI3K/AKT pathway have entered clinical trials or been approved for cancer treatment. For instance, Alpelisib (an PI3K inhibitor) and Capivasertib (an AKT inhibitor) have been approved by the US Food and Drug Administration (FDA) for treating breast cancer23,46. Further studies are needed to determine whether these drugs can be used for the treatment of MASLD.

In conclusion, 10 hub genes associated with MASLD, which are involved mainly in the PI3K/AKT signaling pathway, were identified in our study via bioinformatics methods. These genes can serve as biomarkers for monitoring the initiation and progression of MASLD, providing new means for its early diagnosis, and the PI3K/AKT signaling pathway may become a new therapeutic target for MASLD. We will further conduct cell and animal model experiments to validate our conclusions.