microRNA associated with hepatocyte injury and systemic inflammation may predict adverse outcomes in cirrhotic patients

Abstract

As the global prevalence of chronic liver disease continues to rise, the need to determine which patients will develop end-stage liver disease and require liver transplantation is increasingly important. However, current prognostic models perform sub-optimally. We aim to determine microRNA profiles associated with clinical decompensation and mortality/transplantation within 1 year. We examined microRNA expression profiles in plasma samples from patients across the spectrum of cirrhosis (n = 154), acute liver failure (ALF) (n = 22), sepsis (n = 20) and healthy controls (HC) (n = 20). We demonstrated that a microRNA-based model (miR-24 and -27a) associated with systemic inflammation differentiated decompensated cirrhosis states from compensated cirrhosis and HC (AUC 0.77 (95% CI 0.69-0.85)). 6 patients within the compensated cirrhosis group decompensated the subsequent year and their exclusion improved model performance (AUC 0.81 (95% CI 0.71-0.89)). miR-191 (associated with liver injury) predicted risk of mortality across the cohort when acutely decompensated and acute-on-chronic-liver failure patients were included. When they were excluded miR-24 (associated with systemic inflammation) predicted risk of mortality. Our findings demonstrate that microRNA associated with systemic inflammation and liver injury predict adverse outcomes in cirrhosis. miR-24 and -191 require further investigation as prognostic biomarkers and therapeutic targets for patients with liver disease.

Fig. 1

Heatmap and 2-way hierarchical clustering of miRNA expression across all patient groups. Clustering was performed on all samples including all miRNA expressed in more than a third of samples.

Fig. 2

Principle component analysis of prevalently expressed miRNA in patients with ACLF, AD, ALF and sepsis. Presented with loadings.

Fig. 3

MetaCore™ pathway analysis of prevalently expressed miRNA differentiating decompensated cirrhosis states from cCLD and HC. (A) Network of gene and miRNA expression from MetaCore™ pathway analysis of miRNA which discriminated between compensated and decompensated states. Thick cyan lines indicate fragments of canonical pathways. Up-regulated genes are marked with red circles. Down-regulated genes are marked with blue circles. ‘Checkerboard’ color indicates mixed expression for the gene between files or between multiple tags of the same gene. (B) Processes associated with miRNA from this signature.

Fig. 4

Spearman’s rank correlation matrix of miRNA expression with clinical and laboratory variables. Statistical significance set after correction for false discovery with the Benjamini–Hochberg procedure (p < 0.0344) and designated by *.

Fig. 5

Model discriminating patients with decompensated cirrhosis from those without. (A) Original model (n = 174,AUC 0.77 (95% CI 0.69–0.85, p < 0.0001*). (B) Excluding patients with dCLD (n = 80, AUC 0.76 (95% CI 0.65–0.86, p < 0.0001*). (C) Excluding patients with ACLF and AD (n = 134, AUC 0.78 (95% CI 0.69–0.86, p < 0.0001*). (D) Excluding healthy controls (n = 154, AUC 0.69 (95% CI 0.56–0.81, p = 0.008*). (E) Excluding HC and the 6 cCLD patients who decompensated the year after sampling (n = 148, AUC 0.74 (95% CI 0.60–0.88, p < 0.0001*). (F) Excluding the 6 cCLD patients who decompensated the year after sampling (n = 168, AUC 0.81 (95% CI 0.73–0.89, p < 0.0001*). Statistical significance set after correction for false discovery with the Benjamini–Hochberg procedure (p < 0.0344) and designated by *.