A Novel Signature Combing Cuproptosis- and Ferroptosis-Related Genes in Nonalcoholic Fatty Liver Disease.
- Author:
Rou-Rou FANG
1
;
Qi-Fan YANG
1
;
Jing ZHAO
2
;
Shou-Zhu XU
3
Author Information
- Publication Type:Journal Article
- Keywords: bioinformatics analysis; cuproptosis; ferroptosis; nonalcoholic fatty liver disease
- MeSH: Non-alcoholic Fatty Liver Disease/pathology*; Humans; Ferroptosis/genetics*; Copper/metabolism*; Gene Ontology; Gene Expression Profiling
- From: Chinese Medical Sciences Journal 2024;39(4):261-272
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes.
METHODS:The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the 'limma' package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs. Ferroptosis- and cuproptosis-related genes were obtained from the FerrDb V2 database and available literatures, respectively. A combined signature for cuproptosis- and ferroptosis-related genes, called CRF, was constructed using the STRING database. Hub genes were identified by overlapping DEGs, WGCNA-derived key genes, and combined signature CRF genes, and validated using the GSE109836 and GSE227714 datasets and real-time quantitative polymerase chain reaction. A nomogram of NASH diagnostic model was established utilizing the 'rms' package in R software based on the hub genes, and the diagnostic value of hub genes was assessed using receiver operating characteristic curve analysis. In addition, immune cell infiltration in NASH versus healthy controls was examined using the CIBERSORT algorithm. The relationships among various infiltrated immune cells were explored with Spearman's correlation analysis.
RESULTS:Analysis of GSE89632 identified 236 DEGs between the NASH group and the healthy group. WGCNA highlighted 8 significant modules and 11,095 pivotal genes, of which 330 genes constituted CRF. Intersection analysis identified IL6, IL1B, JUN, NR4A1, and PTGS2 as hub genes. The hub genes were all downregulated in the NASH group, and this result was further verified by the NASH validation dataset and real-time quantitative polymerase chain reaction. Receiver operating characteristic curve analysis confirmed the diagnostic efficacy of these hub genes with areas under the curve of 0.985, 0.941, 1.000, 0.967, and 0.985, respectively. Immune infiltration assessment revealed that gamma delta T cells, M1 macrophages, M2 macrophages, and resting mast cells were predominantly implicated.
CONCLUSIONS:Our investigation underscores the significant association of cuproptosis- and ferroptosis-related genes, specifically IL6, IL1B, JUN, NR4A1, and PTGS2, with NASH. These findings offer novel insights into the pathogenesis of NASH, potentially guiding future diagnostic and therapeutic strategies.
