Characterization of signature trends across the spectrum of non-alcoholic fatty liver disease using deep learning method

Aims: The timely diagnosis of different stages in NAFLD is crucial for disease treatment and reversal. We used hepatocellular ballooning to determine different NAFLD stages.Main methods: We analyzed differentially expressed genes (DEGs) in 78 patients with NAFLD and in healthy controls from previously published RNA-seq data. We identified two expression types in NAFLD progression, calculated the predictive power of candidate genes, and validated them in an independent cohort. We also performed cancer studies with these candidates retrieved from the Cancer Genome Atlas.Key findings: We identified 103 DEGs in NAFLD patients compared to healthy controls: 75 genes gradually increased or decreased in the NAFLD stage, whereas 28 genes showed differences only in NASH. The former were enriched in negative regulation and binding-related genes; the latter were involved in positive regulation and cell proliferation. Feature selection showed the gradual up-or down-regulation of 21 genes in NASH compared to controls; 18 were highly expressed only in NASH. Using deep-learning method with subset of features from lasso regression, we obtained reliable determination performance in NAFL and NASH (accuracy: 0.857) and validated these genes using an independent cohort (accuracy: 0.805). From cancer studies, we identified significant dif-ferential expression of several candidate genes in LIHC; 5 genes were gradually up-regulated and 6 showing high expression only in NASH were influential to patient survival.Significance: The identified biomolecular signatures may determine the spectrum of NAFLD and its relationship with HCC, improving clinical diagnosis and prognosis and enabling a therapeutic intervention for NAFLD.

Automated summary

The timely diagnosis of different stages in NAFLD is crucial for disease treatment and reversal. In this study, we identified differentially expressed genes in NAFLD patients compared to healthy controls and determined their predictive power. We also found significant differential expression of candidate genes in liver cancer that may influence patient survival.