Comprehensive Analysis of NAFLD and the Therapeutic Target Identified - PubMed (original) (raw)
Comprehensive Analysis of NAFLD and the Therapeutic Target Identified
Weiheng Wen et al. Front Cell Dev Biol. 2021.
Abstract
Objective: Non-alcoholic fatty liver disease (NAFLD) is a serious health threat worldwide. The aim of this study was to comprehensively describe the metabolic and immunologic characteristics of NAFLD, and to explore potential therapeutic drug targets for NAFLD. Methods: Six NAFLD datasets were downloaded from the Gene Expression Omnibus (GEO) database, including GSE48452, GSE63067, GSE66676, GSE89632, GSE24807, and GSE37031. The datasets we then used to identify and analyze genes that were differentially expressed in samples from patients with NAFLD and normal subjects, followed by analysis of the metabolic and immunologic characteristics of patients with NAFLD. We also identified potential therapeutic drugs for NAFLD using the Connectivity Map (CMAP) database. Moreover, we constructed a prediction model using minimum depth random forest analysis and screened for potential therapeutic targets. Finally, therapeutic targets were verified in a fatty liver model stimulated by palmitic acid (PA). Results: A total of 1,358 differentially expressed genes (DEGs) were obtained, which were mainly enriched in carbohydrate metabolism, lipid metabolism, and other metabolic pathways. Immune infiltration analysis showed that memory B cells, regulatory T cells and M1 macrophage were significantly up-regulated, while T cells follicular helper were down regulated in NAFLD. These may provide a reference for the immune-metabolism interaction in the pathogenesis of NAFLD. Digoxin and helveticoside were identified as potential therapeutic drugs for NAFLD via the CMAP database. In addition, a five-gene prediction model based on minimum depth random forest analysis was constructed, and the receiver operating characteristic (ROC) curves of both training and validation set reached 1. The five candidate therapeutic targets were ENO3, CXCL10, INHBE, LRRC31, and OPTN. Moreover, the efficiency of hepatocyte adipogenesis decreased after OPTN knockout, confirming the potential use of OPTN as a new therapeutic target for NAFLD. Conclusion: This study provides a deeper insight into the molecular pathogenesis of NAFLD. We used five key genes to construct a diagnostic model with a strong predictive effect. Therefore, these five key genes may play an important role in the diagnosis and treatment of NAFLD, particularly those with increased OPTN expression.
Keywords: NAFLD; immune infiltration; integrated analysis; metabolic pathway; prediction model; therapeutic target.
Copyright © 2021 Wen, Wu, Zhang, Chen, Sun and Chen.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Figures
FIGURE 1
Differentially expressed genes. (A) Schematic diagram of the study design. (B) Two-dimensional PCA plot of the combined expression profile. (C) Volcano of differentially expressed genes. The red dots represent up-regulated genes while the green dots represent down-regulated genes.
FIGURE 2
Pathway analysis of NAFLD pathogenesis. (A) Pathway analysis of differentially expressed genes. The red bar represents the functional pathway enriched by up-regulated genes, while the green bar represents the functional pathway enriched by down-regulated genes. (B) Heatmap of the specific metabolism-associated pathways. (C) Boxplot of the signature score for differentially amino acid metabolism-associated pathways. (D) Boxplot of the signature score for differentially lipid metabolism-associated pathways. (E) Boxplot of the signature score for differentially other metabolism-associated pathways. *p < 0.05, **p < 0.01 and ***p < 0.001.
FIGURE 3
Immune landscape of NAFLD. (A) The percentage of 22 types of immune cells in the NAFLD and control groups. (B) The difference of immune cells between NAFLD and control group. (C) The TSNE algorithm was used for dimensionality reduction and finally 16 cell clusters were successfully classified. (D) All 16 clusters of cells were annotated by Celldex package according to the composition of the marker genes. (E) Enrichment scores of B cells subpopulations at the single-cell level. (F) Enrichment scores of T cells subpopulations at the single-cell level. (G) Enrichment scores of Macrophages subpopulations at the single-cell level. ****p < 0.0001.
FIGURE 4
CMap analysis of potential therapeutic drugs for NAFLD. (A) Results of CMap analysis for differentially expressed genes. (B) Molecular action of potential therapeutic drugs. (C) Expression of 17 drugs target genes in HepG2 cell after stimulated by palmitic acid. *p < 0.05, **p < 0.01 and ***p < 0.001.
FIGURE 5
Construction of NAFLD prediction model. (A) Expression pattern of the identified hub genes. (B) ROC of the training set. (C) ROC of the validation set.
FIGURE 6
Target gene validation in HepG2 cell. (A) Expression of 5 hub genes in HepG2 cell after stimulated by palmitic acid. (B) Efficacy of OPTN knockout in HepG2 cell. (C) Expression of adipogenesis related genes after OPTN knockdown. (D) Expression of other hub genes after OPTN knockdown. (E) Effect of OPTN knockdown on the intracellular triglyceride content in HepG2 cell. (F) Effect of OPTN knockdown on the release of proinflammatory cytokines in HepG2 cell. (G) Metabolic and immune regulation of OPTN. *p < 0.05, **p < 0.01 and ***p < 0.001.
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