Combined with multiplex and network analysis to reveal the key genes and mechanisms of nonalcoholic fatty liver disease

Machine learning deciphers the significance of mitochondrial regulators on the diagnosis and subtype classification in non-alcoholic fatty liver disease

Background

Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent liver disease worldwide and lack of research on the diagnostic utility of mitochondrial regulators in NAFLD. Mitochondrial dysfunction plays a pivotal role in the development and progression of NAFLD, especially oxidative stress and acidity β-oxidative overload. Thus, we aimed to identify and validate a panel of mitochondrial gene expression biomarkers for detection of NAFLD.

Methods

We selected the GSE89632 dataset and identified key mitochondrial regulators by intersecting DEGs, WGCNA modules, and MRGs. Classification of NAFLD subtypes based on these key mitochondrial regulatory factors was performed, and the pattern of immune system infiltration in different NAFLD subtypes were also investigated. RF, LASSO, and SVM-RFE were employed to identify possible diagnostic biomarkers from key mitochondrial regulatory factors and the predictive power was demonstrated through ROC curves. Finally, we validated these potential diagnostic biomarkers in human peripheral blood samples and a high-fat diet-induced NAFLD mouse model.

Results

We identified 25 key regulators of mitochondria and two NAFLD subtypes with different immune infiltration patterns. Four potential diagnostic biomarkers (BCL2L11, NAGS, HDHD3, and RMND1) were screened by three machine learning methods thereby establishing the diagnostic model, which showed favorable predictive power and achieved significant clinical benefit at certain threshold probabilities. Then, through internal and external validation, we identified and confirmed that BCL2L11 was significantly downregulated in NAFLD, while the other three were significantly upregulated.

Conclusion

The four MRGs, namely BCL2L11, NAGS, HDHD3, and RMND1, are novel potential biomarkers for diagnosing NAFLD. A diagnostic model constructed using the four MRGs may aid early diagnosis of NAFLD in clinics.

Identification of key genes and functional enrichment analysis of liver fibrosis in nonalcoholic fatty liver disease through weighted gene co-expression network analysis

Nonalcoholic fatty liver disease (NAFLD) is a common type of chronic liver disease, with severity levels ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). The extent of liver fibrosis indicates the severity of NASH and the risk of liver cancer. However, the mechanism underlying NASH development, which is important for early screening and intervention, remains unclear. Weighted gene co-expression network analysis (WGCNA) is a useful method for identifying hub genes and screening specific targets for diseases. In this study, we utilized an mRNA dataset of the liver tissues of patients with NASH and conducted WGCNA for various stages of liver fibrosis. Subsequently, we employed two additional mRNA datasets for validation purposes. Gene set enrichment analysis (GSEA) was conducted to analyze gene function enrichment. Through WGCNA and subsequent analyses, complemented by validation using two additional datasets, we identified five genes (BICC1, C7, EFEMP1, LUM, and STMN2) as hub genes. GSEA analysis indicated that gene sets associated with liver metabolism and cholesterol homeostasis were uniformly downregulated. BICC1, C7, EFEMP1, LUM, and STMN2 were identified as hub genes of NASH, and were all related to liver metabolism, NAFLD, NASH, and related diseases. These hub genes might serve as potential targets for the early screening and treatment of NASH.