Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and atherosclerosis

• Published in: Frontiers in cardiovascular medicine Vol. 10; p. 1142296
• Main Authors: Mo, Shuangyang, Wang, Yingwei, Yuan, Xin, Wu, Wenhong, Zhao, Huaying, Wei, Haixiao, Qin, Haiyan, Jiang, Haixing, Qin, Shanyu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 30-03-2023
• Subjects: atherosclerosis; bioinformatics; Cardiovascular Medicine; diagnosis model; immune infiltration; machine learning; nonalcoholic fatty liver disease; atherosclerosis; nonalcoholic fatty liver disease; diagnosis model; immune infiltration; machine learning; bioinformatics
• ISSN: 2297-055X; 2297-055X
• DOI: 10.3389/fcvm.2023.1142296

Atherosclerosis (AS) is one of the leading causes of the cardio-cerebral vascular incident. The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.
The common differentially expressed genes (co-DEGs) with a consistent trend were identified
bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established
the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.
A total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971-1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938-0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.
We identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS.