{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16"],"submitter":["Wang Q"],"pubmed_abstract":["<h4>Background</h4>Early-onset preeclampsia (EOPE) is a severe form of preeclampsia that mainly contributes to maternal and perinatal morbidity and mortality worldwide. This study aimed to systematically analyze the expression and function of ferroptosis-related gene HILPDA in EOPE placentas.<h4>Methods</h4>We included five transcriptomic datasets (GSE148241, GSE44711, GSE74341, GSE114691, GSE10588) downloaded from the Gene Expression Omnibus (GEO) in this study. Using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning models (LASSO, SVM-RFE, Random Forest), We identified hub genes and diagnostic biomarkers. We performed functional enrichment (GO and KEGG) and immune infiltration analysis to elucidate molecular mechanisms. Experimen"],"journal":["Frontiers in immunology"],"pagination":["1627057"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12375643"],"repository":["biostudies-literature"],"pubmed_title":["Identification and validation of expression and functions of ferroptosis-related gene HILPDA in early-onset preeclampsia placentas."],"pmcid":["PMC12375643"],"pubmed_authors":["Li X","Wang Q","Ding B","Du D","Tao Y","Yang N","Jiang C","Wang X","Chen H","Fei J","Dou C"],"additional_accession":[]},"is_claimable":false,"name":"Identification and validation of expression and functions of ferroptosis-related gene HILPDA in early-onset preeclampsia placentas.","description":"<h4>Background</h4>Early-onset preeclampsia (EOPE) is a severe form of preeclampsia that mainly contributes to maternal and perinatal morbidity and mortality worldwide. This study aimed to systematically analyze the expression and function of ferroptosis-related gene HILPDA in EOPE placentas.<h4>Methods</h4>We included five transcriptomic datasets (GSE148241, GSE44711, GSE74341, GSE114691, GSE10588) downloaded from the Gene Expression Omnibus (GEO) in this study. Using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning models (LASSO, SVM-RFE, Random Forest), We identified hub genes and diagnostic biomarkers. We performed functional enrichment (GO and KEGG) and immune infiltration analysis to elucidate molecular mechanisms. Experimen","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-07-15T12:47:17.102Z","creation":"2026-07-04T03:16:00.908Z"},"accession":"S-EPMC12375643","cross_references":{"pubmed":["40861438"],"doi":["10.3389/fimmu.2025.1627057"]}}