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Abstract Preeclampsia (PE) is a pregnancy complication involving immune dysregulation. This study aims to identify diagnostic immune biomarkers for PE using machine learning. mRNA expression profiles from GSE75010 and immune-related genes from ImmPort were analyzed. Differentially expressed immune-related genes (DIRGs) were identified and subjected to GO/KEGG enrichment, PPI network, LASSO, and mSVM-RFE analyses.
Abstract Background: High-Grade Serous Ovarian Carcinoma (HGSOC) and Low-Grade Serous Ovarian Carcinoma (LGSOC) are distinct subtypes of epithelial ovarian cancer with significant differences in pathogenesis and prognosis, posing challenges for precise diagnosis. Identifying reliable biomarkers is crucial for improving differential diagnosis and clinical management. Methods: Transcriptome RNA-seq data of HGSOC and LGSOC were obtained from the GEO database (GSE27651, GSE126132).
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