Machine Learning and Experimental Validation Identified Ferroptosis Signature and Innovative Biomarkers (ESR1 and GSTZ1) in Liver Fibrosis

Journal of Inflammation Research, Год журнала: 2024, Номер Volume 17, С. 10313 - 10332

Опубликована: Дек. 1, 2024

Background: Targeting ferroptosis is an effective approach to mitigate hepatic fibrosis, yet no reports exist on the signature in liver fibrosis. This study aimed explore characteristics 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) Support Vector Machine (SVM), used identify core genes fibrosis ferroptosis. WGCNA can pinpoint modules linked clinical traits, aiding discovering diagnostic progression molecules complex diseases. RF SVM are often utilized for validation boost result accuracy. Carbon tetrachloride (CCl4) was establish a mouse model validate gene expression, which also assessed test GEO datasets. Finally, role of hepatocellular carcinoma (HCC) investigated using ROC analysis. Results: methods screened nine genes, IL1B, GSTZ1, LIFR, SLC25A37, PTGS2, MT1G, HSPB1, ESR1, PHGDH. In vivo experimental validation, RT-PCR showed ESR1 GSTZ1 significantly under-expressed group compared normal group. Simultaneously, GSE6764 GSE188604, identified as protective More in-depth research found that exhibited good performance both HCC, suggesting persistent decrease patients might signal HCC. Conclusion: The present first report identifies two novel biomarkers, providing new insights diagnosis treatment future. Keywords: ferroptosis, biomarker

Язык: Английский

IRG1/Itaconate inhibits hepatic stellate cells ferroptosis and attenuates TAA-induced liver fibrosis by regulating SLC39A14 expression

International Immunopharmacology, Год журнала: 2024, Номер 146, С. 113945 - 113945

Опубликована: Дек. 25, 2024

Язык: Английский