Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

Abstract

Background: Liver cirrhosis is characterized by high mortality, bringing a serious health and economic burden to the world. The clinical manifestations of liver cirrhosis are complex and heterogeneous. According to subgroup characteristics, identifying cirrhosis has become a challenge. Objectives: The purpose of this study was to evaluate the difference between different subgroups of cirrhosis. The ultimate goal of research on these different phenotypes was to discover groups of patients with unique treatment characteristics, and formulate targeted treatment plans that improve the prognosis of the disease and improve the patients’ quality of life. Methods: We obtained the relevant gene chip by searching the gene expression omnibus (GEO) database. According to the gene expression profile, 79 patients with liver cirrhosis were divided into four subgroups, which showed different expression patterns. Therefore, we used weighted gene coexpression network analysis (WGCNA) to find differences between subgroups. Results: The characteristics of the WGCNA module indicated that subjects in subgroup I might exhibit inflammatory characteristics; subjects in subgroup II might exhibit metabolically active characteristics; arrhythmogenic right ventricular cardiomyopathy and neuroactive ligand-receptive somatic interaction pathways were significantly enriched in subgroup IV. We did not find a significantly upregulated pathway in the third subgroup. Conclusions: In this study, a new type of clinical phenotype classification of liver cirrhosis was derived by consensus clustering. This study found that patients in different subgroups may have unique gene expression patterns. This new classification method helps researchers explore new treatment strategies for cirrhosis based on clinical phenotypic characteristics.