Bioinformatics analysis of potential ferroptosis and non- alcoholic fatty liver disease biomarkers

Author:

Yu Xiaoxiao1,Yang Kai1,Fang Zhihao1,Zhu Tong1,Liu Changxu1,Guo Zihao1,Dong Zhichao1,Liu Chang1

Affiliation:

1. Fourth Affiliated Hospital of Harbin Medical University

Abstract

Abstract Background: Ferroptosis plays a crucial role in the development of non-alcoholic fatty liver disease (NAFLD). In this study, we aimed to use a comprehensive bioinformatics approach and experimental validation to identify and verify potential ferroptosis-related genes in NAFLD. Materials and methods: We downloaded the microarray datasets from the Gene Expression Omnibus database for screening differentially expressed genes (DEGs) and identified the intersection of these datasets with ferroptosis-related DEGs from the Ferroptosis database. Subsequently, ferroptosis-related DEGs were obtained using support vector machine analysis; the least absolute shrinkage and selection operator algorithm was then used to identify six marker genes. Furthermore, the CIBERSORT algorithm was used to estimate the proportion of different types of immune cells. Subsequently, we constructed drug regulatory networks and ceRNA regulatory networks. Lastly, we validated our findings in a mouse model of NAFLD induced by a high-fat diet. Results: We identified EGR1, IL6, JUN、SOCS1, NR4A1, and ZFP36 as marker genes for NAFLD, demonstrating their robust diagnostic abilities. Subsequent functional enrichment analysis results revealed that these marker genes were associated with multiple diseases and play a key role in NAFLD via the regulation of immune response and amino acid metabolism, among other pathways. The presence of immune infiltration implies that alterations in the immune microenvironment of NAFLD patients may be associated with the marker genes. Moreover, we identified 53 drugs targeting the 6 marker genes and ceRNA networks, which revealed complex regulatory relationships. The expression of hepatic EGR1, IL6, SOCS1, and NR4A1 was significantly downregulated in the NAFLD model. Conclusion: Here, we analyzed and validated a ferroptosis gene signature in NAFLD using bioinformatics and animal models. Our findings provide new insights and molecular clues for understanding and treating NAFLD. Further studies are needed to assess the diagnostic potential of these markers for NAFLD.

Publisher

Research Square Platform LLC

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