ArticleJournal of inflammation research2024
Machine Learning and Experimental Validation Identified Ferroptosis Signature and Innovative Biomarkers (ESR1 and GSTZ1) in Liver Fibrosis.
Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Targeting ferroptosis is an effective approach to mitigate hepatic fibrosis, yet no reports exist on the ferroptosis signature in liver fibrosis. This study aimed to explore ferroptosis characteristics in this disease. Methods: RNAseq data from GSE6764, GSE188604 and Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) were downloaded. Multiple machine learning methods, including Weighted Gene Co-expression Network Analysis (WGCNA), Random Forest (RF) and Support Vector Machine (SVM), were used to identify core genes in liver fibrosis and ferroptosis. WGCNA can pinpoint modules linked to clinical traits, aiding in discovering diagnostic and progression molecules in complex diseases. RF and SVM are often utilized for WGCNA validation to boost result accuracy. Carbon tetrachloride (CCl4) was used to establish a mouse liver fibrosis model to validate core gene expression, which was also assessed in test and validation GEO datasets. Finally, the diagnostic role of the core genes in liver fibrosis and hepatocellular carcinoma (HCC) was also investigated using ROC analysis. Results: Multiple machine learning methods screened nine core genes, including IL1B, GSTZ1, LIFR, SLC25A37, PTGS2, MT1G, HSPB1, ESR1, and PHGDH. In vivo experimental validation, RT-PCR showed ESR1 and GSTZ1 were significantly under-expressed in the liver fibrosis group compared to the normal group. Simultaneously, in GSE6764 and GSE188604, ESR1 and GSTZ1 were also identified as protective genes for liver fibrosis. More in-depth research found that ESR1 and GSTZ1 exhibited a good diagnostic performance both in liver fibrosis and HCC, suggesting that a persistent decrease in ESR1 and GSTZ1 in patients might signal the progression from hepatic fibrosis to HCC. Conclusion: The present study is the first to report the ferroptosis signature in liver fibrosis and identifies two novel biomarkers, ESR1 and GSTZ1, providing new insights for the diagnosis and treatment of liver fibrosis in the future.
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