AUTHOR=Bai Lilian , Guo Yanyan , Gong Junxing , Li Yuchen , Huang Hefeng , Meng Yicong , Liu Xinmei 

TITLE=Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in preeclampsia

JOURNAL=Frontiers in Physiology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2023.1078166

DOI=10.3389/fphys.2023.1078166

ISSN=1664-042X

ABSTRACT=The aim of this study was to determine the placenta markers for preeclampsia and elucidate the importance of immune cell infiltration in the pathogenesis of preeclampsia. Gene expression profiles from the Gene Expression Omnibus database and differentially expressed genes of 30 preeclampsia and 27 control samples were screened and validated against other datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. A total of 27 genes, mainly involved in reproductive structure and system development and hormone transport and metabolism, were identified. Enrichment analysis revealed emphasis on cytokine–cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases, preeclampsia, and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placenta markers for preeclampsia and are associated with various immune cells, which may contribute to the diagnosis and treatment of preeclampsia and exploration of pathophysiological mechanisms.