Exploring the Immune Landscape of Cirrhosis through Weighted Gene Co-expression Network Analysis

Immune Landscape of Cirrhosis through Gene Analysis

Abstract

Cirrhosis is a persistent hepatic ailment that emerges from a range of causes, including viral infections, alcoholic liver disease, and non-alcoholic fatty liver disease. It is distinguished by the replacement of normal liver parenchyma with fibrous scar tissue, culminating in the development of hepatic insufficiency, portal hypertension, and eventual liver collapse. Several molecular and cellular mechanisms contribute to cirrhosis' pathogenesis, including activation of immune cells and dysregulation of immune-related pathways. Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful data mining application used to identify gene modules and hub genes that are closely associated with specific phenotypes or conditions of interest. In this study, we performed WGCNA on publicly available gene expression datasets and subsequently assessed the roles of immune-related genes in the etiology and progression of cirrhosis, intending to explore potential therapeutic targets for this disease. GSE36411 gene expression profiling was extracted from the Gene Expression Omnibus repository (GEO). The transcriptomic data were submitted to Weighted Gene Co-expression Network Analysis (WGCNA) to screen for the presence of key genes, and immune-related genes were filtered by comparison to the InateDB database. Cancer Genome Atlas (TCGA) was included in the study to validate the significant modules generated from WGCNA. The key gene interaction network was constructed using GeneMANIA and Metascape. Kaplan-Meier method and Spearman correlation were used to evaluate the correlation of immune-related genes with prognosis, tumor microenvironment, and immune cell infiltration. Finally, we explored a possible mechanism using gene set enrichment (GSEA) analyses. In total, 2,102 differentially expressed genes (DEGs) were identified from the gene expression profile dataset. A weighted gene co-expression network analysis was performed, resulting in the classification of genes into 3 modules. Among these modules, the turquoise module was found to be most closely associated with cirrhosis. By comparing the turquoise module genes with an InateDB immune-related gene set, we identified 157 immune-associated genes. In addition, our study found that many hub genes are strongly associated with the number of immune-related genes in liver cirrhosis, in addition to a few modules associated with immune infiltration. It turns out that these hub genes were engaged in migration, activation, and immune cell regulation, as well as in the signaling pathways that drive the immune response to infection. Our research offered a deeper understanding of the underlying processes of immune infiltration in cirrhosis and also suggested potential treatment options for this troublesome condition. Our results demonstrate the effectiveness of WGCNA in uncovering new knowledge regarding the biology of cirrhosis and the function of the immune system in this disease. More studies ought to focus on the validation of the identified hub genes and the determination of their clinical relevance. These results could serve as the basis for the creation of more potent therapies for those with liver cancer linked to cirrhosis.