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Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers.

ABSTRACT: Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease that presents a great challenge for treatment and prevention.. This study aims to implement a machine learning approach that employs such datasets to identify potential biomarker targets. We developed a pipeline to identify potential biomarkers for NAFLD that includes five major processes, namely, a pre-processing step, a feature selection and a generation of a random forest model and, finally, a downstream feature analysis and a provision of a potential biological interpretation. The pre-processing step includes data normalising and variable extraction accompanied by appropriate annotations. A feature selection based on a differential gene expression analysis is then conducted to identify significant features and then employ them to generate a random forest model whose performance is assessed based on a receiver operating characteristic curve. Next, the features are subjected to a downstream analysis, such as univariate analysis, a pathway enrichment analysis, a network analysis and a generation of correlation plots, boxplots and heatmaps. Once the results are obtained, the biological interpretation and the literature validation is conducted over the identified features and results. We applied this pipeline to transcriptomics and lipidomic datasets and concluded that the C4BPA gene could play a role in the development of NAFLD. The activation of the complement pathway, due to the downregulation of the C4BPA gene, leads to an increase in triglyceride content, which might further render the lipid metabolism. This approach identified the C4BPA gene, an inhibitor of the complement pathway, as a potential biomarker for the development of NAFLD.

SUBMITTER: Shafiha R

PROVIDER: S-EPMC8615894 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNon-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide. Autophagy plays a vital role in NAFLD development and progression. We aimed to establish a novel autophagy-related gene (ARG) signature as a therapeutic target in NAFLD patients based on high-throughput sequencing data.MethodsARGs obtained from the HAMdb and high-sequencing data obtained from the Gene Expression Omnibus (GEO) database were analyzed to identify differentially expressed ARGs (DEARGs) between normal and NASH tissues. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore potential biological and pathological functions of DEARGs. The protein-protein interaction (PPI) network of the DEARGs was established through the STRING website, and visualized by Cytoscape. In addition, hub genes were validated by an independent dataset GSE89632. Finally, we performed Gene Set Variation Analysis (GSVA) pathway-related analysis to identify the pivotal signaling pathways and genes for the progression of non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH).ResultsA total of 76 DEARGs were identified in the GSE126848 dataset, of which 45 genes were upregulated and 31 genes were downregulated. GO analysis showed that the biological functions of DEARGs focused primarily on autophagy, cellular response to external stimulus, fibroblast proliferation, late endosome, and ubiquitin protein ligase binding. KEGG pathway analysis showed that these DEARGs were mainly involved in the apoptosis, PI3K-Akt signaling pathway, and estrogen signaling pathway. Among DEGs, 9 most closely related genes were identified from the PPI network. Furthermore, NOS3, IGF1, VAMP8, FOS, and HMOX1 were verified in the GSE89632 dataset. At last, the MAPK signal pathway was identified as important pathway, and JUN was identified as a key gene involved in the progression from NAFL to NASH.ConclusionThis study may provide credible molecular biomarkers in terms of screening and diagnosis for NAFLD. Meanwhile, it also serves as a basis for exploring the molecular mechanisms underlying the progression of NAFL to NASH.

Project description:Background: It is unknown how perceived social support and the progression of liver damage influence the psychosocial profile of patients with non-alcoholic fatty liver disease (NAFLD). In the present study, we therefore investigated which biomarkers influence the quality of life, mental health, and coping strategies of NAFLD patients. Methods: Quality of life (SF-12 and CLDQ-NAFLD), mental health (HADS and BDI-II), and coping strategies (COPE-28) were evaluated by high or low perceived social support (MSPSS) and the presence of non-alcoholic steatohepatitis (NASH) and significant fibrosis in 492 biopsy-proven NAFLD patients. The results were compared with quality of life normality tables for the general Spanish population. We also determined whether liver histology and biopsychosocial variables predicted participants' quality of life. Results: Interactive effects were found in vitality (p = 0.05), activity (p = 0.005), anxiety (p = 0.04), and denial (p = 0.04), with NASH patients showing a higher-risk biopsychosocial profile when they perceived less social support. Furthermore, patients with low perceived social support showed lower quality of life, worse mental health, and more maladaptive coping than those with high perceived social support, regardless of NASH presence. Patients with significant fibrosis showed lower quality of life compared to those without or the general Spanish population. Patients with significant fibrosis also reported worse mental health and more maladaptive coping. Lastly, significant fibrosis, female sex, greater anxiety and depressive symptoms, and worse physical and mental health-related quality of life were found to be independent determinants of worse disease-specific quality of life in these patients. Conclusions: Low perceived social support, significant fibrosis, and female sex were independently associated with a higher-risk psychosocial profile in NAFLD. These findings support the role of psychological biomarkers based on quality of life, mental health, and coping strategies in the management of these patients and suggest the potential benefits of a psychological intervention.

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Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers.

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